Computer Vision and Image Processing, 3rd Edition

Topic Information

Dear Colleagues,

The field of computer vision and image processing has advanced significantly in recent years, with new techniques and applications emerging constantly. Building on the successes of our first and second edition, we are pleased to announce a third edition on this exciting topic. We invite researchers, academics, and practitioners to submit original research articles, reviews, or case studies that address the latest developments in computer vision and image processing. Topics of interest include but are not limited to:

• Deep learning for image classification and recognition
• Object detection and tracking
• Image segmentation and analysis
• 3D reconstruction and modeling
• Image and video compression
• Image enhancement and restoration
• Medical image processing and analysis
• Augmented and virtual reality

Submissions should be original and should not have been published or submitted elsewhere. All papers will be peer-reviewed by at least two experts in the field, and accepted papers will be published together on the topic website. To submit your paper, please visit the journal's website and follow the submission guidelines. For any queries, please contact the topic editors of the topic.

We look forward to receiving your submissions and sharing the latest advancements in computer vision and image processing with our readers.

Prof. Silvia Liberata Ullo
Prof. Dr. Li Zhang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400	Submit
Electronics electronics	2.9	7.0	2012	14.8 Days	CHF 2400	Submit
Journal of Imaging jimaging	3.8	7.3	2015	21.3 Days	CHF 1800	Submit
Modelling modelling	1.8	2.4	2020	22.7 Days	CHF 1200	Submit
Remote Sensing remotesensing	4.3	9.4	2009	22 Days	CHF 2700	Submit

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Published Papers (19 papers)

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22 pages, 6335 KB  

Open AccessArticle

PIP-PACA: An Interpretable Image Classification Framework via Prototype-Aware Clustering Attention

by Xinyuan Jia, Yanling Li and Yihui Wang

J. Imaging 2026, 12(7), 308; https://doi.org/10.3390/jimaging12070308 (registering DOI) - 8 Jul 2026

Abstract

Image classification interpretability remains a fundamental challenge in the field of computer vision. Despite the remarkable improvements achieved by deep neural networks in classification accuracy, their decision-making processes are often opaque, which limits their applicability in high-stakes scenarios requiring reliability and transparency. Prototype-based [...] Read more.

Image classification interpretability remains a fundamental challenge in the field of computer vision. Despite the remarkable improvements achieved by deep neural networks in classification accuracy, their decision-making processes are often opaque, which limits their applicability in high-stakes scenarios requiring reliability and transparency. Prototype-based methods, such as PIP-Net, address this issue by establishing explicit correspondences between input images and semantic prototypes, thereby enabling an intuitive, evidence-based reasoning paradigm. However, these approaches still suffer from insufficient global context modeling and underutilization of structural relationships among prototypes. To address these limitations, this paper proposes an interpretable image classification model termed PIP-PACA, which is built upon a prototype-aware clustering attention mechanism. In contrast to conventional Transformer architectures based on self-attention, the proposed PACA module introduces a set of learnable cluster centers to project feature representations into a prototype space. Global information is then captured via a bidirectional attention mechanism between features and prototypes. This design is inherently aligned with the principles of prototype learning while reducing the computational complexity from quadratic to linear. Furthermore, a normalization operation is incorporated during the feature extraction stage to enhance the stability of feature distributions and improve the reliability of prototype matching. Extensive experimental results demonstrate that the proposed method not only preserves the interpretability of the original framework but also achieves notable improvements in classification accuracy, sparsity, and prototype purity. These findings validate the effectiveness and superiority of the clustering-based attention mechanism within the prototype learning paradigm. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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25 pages, 28692 KB  

Open AccessArticle

Semi-Supervised Degradation-Aware Learning for All-in-One Weather-Degraded Image Restoration

by Lei Cai, Fang Ruan, Wei Lu, Qi Lin, Huijie Zheng, Wenjie Xiang and Tao Zhu

Electronics 2026, 15(12), 2686; https://doi.org/10.3390/electronics15122686 - 17 Jun 2026

Viewed by 170

Abstract

All-in-one weather-degraded image restoration aims to restore clean images from diverse weather-degraded observations (such as rain, haze, and snow) using a unified model. However, this topic remains challenging due to its ill-posed nature and the scarcity of large-scale paired training data. This article [...] Read more.

All-in-one weather-degraded image restoration aims to restore clean images from diverse weather-degraded observations (such as rain, haze, and snow) using a unified model. However, this topic remains challenging due to its ill-posed nature and the scarcity of large-scale paired training data. This article develops a novel semi-supervised learning framework, termed Semi-Supervised Degradation-Aware Learning (S²DAL), to adjust the feature space to align with the unified parameter space for all-in-one adverse weather removal. Specifically, the proposed S²DAL consists of two backbone networks: a Degradation-guided Histogram Transformer (DHformer) for weather-degraded image restoration and a Degradation-guided Convolutional Neural Network (DCNN) for degradation generation. A key component, the Degradation-guided Histogram Transformer (DHT) block, is designed to effectively capture intrinsic image features while suppressing diverse degradation interference through channel shuffling modulation, dynamic-range histogram self-attention, and dual-scale gated feed forward. Furthermore, a Monte Carlo-based Expectation-Maximization (EM) algorithm is introduced to jointly optimize latent variables and network parameters under both labeled and unlabeled data. Extensive quantitative and qualitative results on synthetic and real-world datasets consistently demonstrate that the proposed S²DAL achieves superior restoration performance compared to multiple state-of-the-art fully supervised and semi-supervised approaches. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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23 pages, 11802 KB  

Open AccessArticle

LE-DETR: A Lightweight and Efficient Model for Small-Object Detection in Remote Sensing Images

by Qi Wang, Hongyun An and Yongji Chen

Remote Sens. 2026, 18(12), 2018; https://doi.org/10.3390/rs18122018 - 17 Jun 2026

Viewed by 323

Abstract

Object detection in remote sensing imagery plays an irreplaceable role in critical fields such as military reconnaissance and disaster monitoring. However, when dealing with minute targets characterised by an extremely low pixel proportion, a lack of textural information, and severe background interference, existing [...] Read more.

Object detection in remote sensing imagery plays an irreplaceable role in critical fields such as military reconnaissance and disaster monitoring. However, when dealing with minute targets characterised by an extremely low pixel proportion, a lack of textural information, and severe background interference, existing algorithms still face the challenge of balancing detection accuracy with computational efficiency. To address this, this paper proposes a lightweight frequency-domain-aware end-to-end detection model, LE-DETR, based on an improved version of RT-DETR. Firstly, a Lightweight Feature Extraction Module (LFEM) is designed. Through a heterogeneous dual-path architecture and reparameterisation techniques, it significantly reduces computational complexity whilst enhancing the capture of fine-grained spatial features. Secondly, an Efficient Spatio-Frequency Fusion Module (ESFFM) is introduced. This utilises a multi-head self-attention mechanism to construct a global view whilst combining the Fourier transform to reconstruct target features from a frequency-domain perspective, thereby effectively suppressing background noise and enhancing the target’s edge signals. Finally, we propose the Efficient Frequency-Aware Fusion Feature Pyramid Network (EFAM-FPN), which utilises SPD Conv to mitigate the loss of key features during downsampling and introduces a frequency-domain attention mechanism to suppress complex background noise, thereby improving the model’s detection accuracy for extremely small objects. The experimental results show that, whilst reducing the number of parameters by 41.7% compared to the baseline model, LE-DETR achieved improvements of 2.6%, 1.7% and 2.4%, respectively, in the mAP50 metric across the three mainstream remote sensing datasets—VisDrone2019, NWPU VHR-10 and DIOR. This demonstrates an effective balance between detection accuracy and inference efficiency, fully validating its robustness and practical value in complex remote sensing application scenarios. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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22 pages, 43415 KB  

Open AccessArticle

FSSM: Frequency-Enhanced State Space Modeling with FFT-Based Two-Sided Non-Causal Convolution for Image Dehazing

by Li Zeng and Yinqing Huang

J. Imaging 2026, 12(6), 260; https://doi.org/10.3390/jimaging12060260 - 13 Jun 2026

Viewed by 262

Abstract

Image dehazing is a fundamental visual restoration task for improving visual perception under low-visibility weather conditions, especially in UAV-based remote sensing, traffic monitoring, and surveillance scenarios. Existing convolutional neural networks are effective in local feature extraction but remain limited in long-range dependency modeling, [...] Read more.

Image dehazing is a fundamental visual restoration task for improving visual perception under low-visibility weather conditions, especially in UAV-based remote sensing, traffic monitoring, and surveillance scenarios. Existing convolutional neural networks are effective in local feature extraction but remain limited in long-range dependency modeling, while Transformer-based methods improve global modeling at the cost of high computational complexity. To address these issues, this paper proposes an efficient image-dehazing framework termed FSSM, which integrates frequency-enhanced State Space Modeling with a hierarchical encoder–decoder architecture. Specifically, an FFT-based State Space Block (FFTSSB) is designed to reformulate state propagation as frequency-domain two-sided non-causal convolution, enabling efficient bidirectional global dependency modeling without explicit recursive scanning. Furthermore, a Frequency-Aware Discriminative Enhancement Block (FDEB) is introduced to enhance local textures, edges, and structural details through spatial gating and lightweight block-wise frequency modulation. Based on these two components, a Frequency-Aware State Interaction (FASI) block is constructed to progressively couple global state propagation and local frequency-aware enhancement. Experimental results on the HazyDet dataset demonstrate that FSSM achieves favorable restoration accuracy, structural consistency, and perceptual quality compared with representative dehazing methods. Ablation studies further validate the effectiveness of the proposed two-sided FFT-based state modeling, frequency-aware enhancement, and hierarchical multi-scale design. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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29 pages, 4494 KB  

Open AccessArticle

IC-SAM: Segment Anything for Industrial Integrated Circuit Chip Inspection

by Fuqin Deng, Zhi Xu, Yihang Shi, Nannan Li, Qingshan Xia and Lanhui Fu

Electronics 2026, 15(11), 2488; https://doi.org/10.3390/electronics15112488 - 5 Jun 2026

Viewed by 251

Abstract

Industrial integrated circuit (IC) chip defect inspection is hindered by diverse topologies, micro-scale structures, and the insufficient precision of traditional vision paradigms. While the Segment Anything Model (SAM) offers strong zero-shot capabilities, its heavy reliance on manual prompts and lack of domain adaptability [...] Read more.

Industrial integrated circuit (IC) chip defect inspection is hindered by diverse topologies, micro-scale structures, and the insufficient precision of traditional vision paradigms. While the Segment Anything Model (SAM) offers strong zero-shot capabilities, its heavy reliance on manual prompts and lack of domain adaptability limit its viability in automated production lines. This paper proposes IC-SAM, a highly automated framework tailored for electronic manufacturing quality control. IC-SAM synergistically integrates three core modules: Process Prior Knowledge (PPK), which embeds semiconductor domain constraints to suppress background noise; Self-Driven Semantic Prompting, which leverages CLIP to align visual features with process descriptions for autonomous target localization; and Global Feature Fusion (GFF), which optimizes boundary localization through multi-scale interaction. Extensive experiments demonstrate that IC-SAM outperforms baseline models by approximately 15% in both mIoU and mBIoU across SIC, CIC, and IGBT datasets. The framework achieves 9.6 FPS under the tested 1024 × 1024 input setting while introducing only 2.3 M learnable parameters, indicating a parameter-efficient adaptation strategy with quantified computational cost for precision IC inspection. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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21 pages, 26964 KB  

Open AccessArticle

DTKD: Diffusion-to-Transformer Heterogeneous Knowledge Distillation for Efficient and Perceptually Enhanced Super-Resolution

by Jeong Hyeok Park and Byung Cheol Song

Electronics 2026, 15(10), 1986; https://doi.org/10.3390/electronics15101986 - 7 May 2026

Viewed by 321

Abstract

Single-image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs and remains fundamentally ill-posed due to the inherent ambiguity of missing high-frequency details. While diffusion-based SR models achieve superior perceptual quality through iterative denoising, their multi-step sampling process results in [...] Read more.

Single-image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs and remains fundamentally ill-posed due to the inherent ambiguity of missing high-frequency details. While diffusion-based SR models achieve superior perceptual quality through iterative denoising, their multi-step sampling process results in substantial computational cost and latency. In contrast, transformer-based SR models offer efficient single-forward inference but are typically optimized for distortion-oriented objectives, limiting perceptual realism. In this paper, we propose DTKD, a diffusion-to-transformer heterogeneous knowledge distillation framework that transfers the perceptual prior of a diffusion teacher into an efficient transformer student. To effectively bridge the representational gap between generative diffusion outputs and deterministic transformer reconstructions, we introduce a frequency-group-aware distillation loss based on two-level discrete wavelet transform (DWT). The loss decomposes images into structured frequency sub-bands and assigns non-uniform weights to emphasize discrepancy-sensitive mid-frequency components. Furthermore, we adopt a progressive scheduling strategy that gradually increases the distillation weight during training to stabilize optimization and balance structural fidelity with perceptual enhancement. Extensive experiments on real-world SR benchmarks demonstrate that the proposed framework consistently improves perceptual quality over a standalone transformer student while maintaining transformer-level inference efficiency. Ablation studies further validate the importance of moderate frequency decomposition, discrepancy-aware weighting, and progressive distillation scheduling. These results suggest that heterogeneous distillation provides an effective and practical approach for transferring diffusion-based generative priors into efficient super-resolution models. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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22 pages, 48062 KB  

Open AccessArticle

MMIC: A Remote Sensing Image Compression Algorithm

by Longwei Li and Likun Hu

Appl. Sci. 2026, 16(9), 4499; https://doi.org/10.3390/app16094499 - 3 May 2026

Viewed by 360

Abstract

Unlike natural images, remote sensing images have unique characteristics such as high spatial resolution, complex textures, and strong directional features. Their content often contains many man-made targets with clear directional structures, such as buildings, roads, and bridges. It also contains complex ground object [...] Read more.

Unlike natural images, remote sensing images have unique characteristics such as high spatial resolution, complex textures, and strong directional features. Their content often contains many man-made targets with clear directional structures, such as buildings, roads, and bridges. It also contains complex ground object boundaries. However, most existing image compression methods are designed for natural images. They typically use square convolution kernels and local receptive fields. As a result, they struggle to effectively capture the rich directional structures in remote sensing images and model global context information. This limits compression efficiency and the fidelity of key information. To address this challenge, this paper proposes a novel remote sensing image compression algorithm. The algorithm uses a multi-scale asymmetric convolution block that combines sampling convolution, parallel one-dimensional horizontal and vertical convolutions, and two-dimensional square convolution. This helps the model better capture directional objects and multi-scale features. In addition, we also propose a multi-scale non-local attention module. It models global dependencies with a linear computational complexity. This helps improve the ability to retain key information. The experimental results demonstrate that compared with the baseline model, the proposed algorithm achieves a 0.40 dB improvement in BD-PSNR and a 10.27% reduction in BD-Rate, while also delivering superior subjective visual quality. These results confirm the effectiveness of our approach for remote sensing image compression. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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18 pages, 28808 KB  

Open AccessArticle

Automatic Polygon Annotation of Plant Objects for Training Dataset Preparation in Green Biomass Segmentation Tasks

by Evgeniy Ivliev, Valery Gvindjiliya, Danila Donskoy and Yevgeniy Chayka

J. Imaging 2026, 12(5), 192; https://doi.org/10.3390/jimaging12050192 - 30 Apr 2026

Viewed by 605

Abstract

This paper addresses the problem of automated segmentation of plant green biomass in field crop images aimed at improving the accuracy of crop and weed identification. To construct a training dataset for neural network models, an automatic annotation algorithm is proposed, enabling the [...] Read more.

This paper addresses the problem of automated segmentation of plant green biomass in field crop images aimed at improving the accuracy of crop and weed identification. To construct a training dataset for neural network models, an automatic annotation algorithm is proposed, enabling the generation of polygonal object masks without human intervention. The method is based on adaptive analysis of color characteristics of plant fragments with iterative narrowing of the hue range in the HSV color space, combined with an integral quality metric that accounts for the dynamics of contour area and shape. The proposed method achieved an IoU of 93.22% and a DSC of 96.30%, demonstrating a high level of agreement between automatic and manual annotations. The generated masks are used to train segmentation models of the YOLO11-seg family. Models of different scales (n, s, m, l, x) were trained and evaluated using standard metrics, including Intersection over Union (IoU), mAP@0.5, mAP@0.5–0.95, F1-score, and Precision–Recall (PR) curves. Experimental results demonstrate that models trained on automatically generated annotations achieve stable segmentation performance of plant green biomass. The best results were obtained with the YOLO11m-seg model, achieving an F1-score of 0. 772. The results confirm the effectiveness of the proposed approach and demonstrate acceptable segmentation quality, supported by both quantitative metrics and visual analysis. The developed automatic annotation algorithm can be used to expand training datasets in computer vision tasks for agricultural applications. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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18 pages, 8455 KB  

Open AccessArticle

LSD-YOLO: A Lightweight Multi-Scale Fusion Network for Railway Insulator Defect Detection

by Jiahao Liu, Lu Yu, Hexuan Ma and Junjie Yu

Appl. Sci. 2026, 16(9), 4185; https://doi.org/10.3390/app16094185 - 24 Apr 2026

Cited by 1 | Viewed by 447

Abstract

To address the challenges of multi-scale defect perception and complex background interference in railway insulator detection, this paper proposes LSD-YOLO, a lightweight multi-scale fusion network based on an improved YOLO11n. The model integrates three core modules: a Large-Small (LS) module for multi-scale receptive [...] Read more.

To address the challenges of multi-scale defect perception and complex background interference in railway insulator detection, this paper proposes LSD-YOLO, a lightweight multi-scale fusion network based on an improved YOLO11n. The model integrates three core modules: a Large-Small (LS) module for multi-scale receptive field fusion, a Token Statistics Self-Attention (TSSA) module for efficient global context modeling, and a Detail-Preserving Contextual Fusion (DPCF) module for adaptive multi-scale feature fusion. Experiments on a multi-defect insulator dataset constructed from 4C inspection system images and public datasets show LSD-YOLO achieves 86.2% mAP@50, 4.1 percentage points higher than the baseline model. Its precision and recall reach 91.8% and 80.6% respectively, with only 2.30 M parameters. Its comprehensive detection performance outperforms mainstream comparative models. The proposed method enhances the integrated detection ability for both physical defects and pollution-flashover faults of insulators, and provides a reference for intelligent inspection in complex railway scenarios. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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25 pages, 4032 KB  

Open AccessArticle

CoFiWaveMamba: A Coarse-to-Fine Wavelet-Guided Mamba Network for Single Image Dehazing

by Qiang Fu, Boyu Lu and Chongyao Yan

Electronics 2026, 15(8), 1599; https://doi.org/10.3390/electronics15081599 - 11 Apr 2026

Cited by 1 | Viewed by 420

Abstract

Single image dehazing remains challenging because haze simultaneously distorts global illumination, scene structure, and fine textures, making rigid low–high frequency decoupling prone to error propagation and detail inconsistency. To address this issue, we propose CoFiWaveMamba, a coarse-to-fine wavelet-guided Mamba network for single image [...] Read more.

Single image dehazing remains challenging because haze simultaneously distorts global illumination, scene structure, and fine textures, making rigid low–high frequency decoupling prone to error propagation and detail inconsistency. To address this issue, we propose CoFiWaveMamba, a coarse-to-fine wavelet-guided Mamba network for single image dehazing. The proposed method first employs wavelet decomposition to separate low- and high-frequency components. For low-frequency restoration, a 2D selective-scan Mamba-based module is introduced to capture long-range dependencies, combined with lightweight high-frequency-guided spatial modulation and Shuffle-guided Sequence Attention, we design a progressive coarse-to-fine refinement strategy that combines Fourier-domain global spectral consistency with wavelet-domain directional detail representation, enabling more targeted recovery of edges and textures. Experiments on synthetic and real dehazing benchmarks, including Haze4K, RESIDE-6K, HSTS-SYNTHETIC, I-Haze, NH-Haze, Dense-Haze, and O-HAZE, as well as ablation studies, verify the effectiveness of the proposed design. Overall, CoFiWaveMamba provides a more coordinated solution for global haze removal and local detail reconstruction, helping suppress residual haze, ringing artifacts, oversharpening, and texture inconsistency while restoring clearer and more natural images. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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26 pages, 12156 KB  

Open AccessArticle

Precision Micro-Vibration Measurement for Linear Array Imaging via Complex Morlet Wavelet Phase Magnification

by Meiyi Zhu, Dezhi Zheng, Ying Zhang and Shuai Wang

Appl. Sci. 2026, 16(7), 3518; https://doi.org/10.3390/app16073518 - 3 Apr 2026

Viewed by 440

Abstract

Traditional vision-based vibration measurement is fundamentally constrained by the low sampling rates of area-scan cameras and the noise sensitivity of existing motion magnification algorithms. To overcome these spatiotemporal barriers, we propose a high-fidelity framework that integrates ultra-high-speed line-scan imaging with a 1D Complex [...] Read more.

Traditional vision-based vibration measurement is fundamentally constrained by the low sampling rates of area-scan cameras and the noise sensitivity of existing motion magnification algorithms. To overcome these spatiotemporal barriers, we propose a high-fidelity framework that integrates ultra-high-speed line-scan imaging with a 1D Complex Morlet Wavelet Phase-Based Video Magnification (CMW-PVM) algorithm. By extracting and manipulating the localized phase of 1D spatial signals, CMW-PVM effectively decouples structural dynamics from background noise while eliminating the computational redundancy associated with 2D spatial pyramid methods. Simulations demonstrate that CMW-PVM significantly extends the linear magnification range (up to $\alpha \approx 35$) while preserving exceptional structural fidelity (FSIM $>0.87$) under severe noise conditions (SNR = 10 dB). Experimental validation against a laser Doppler vibrometer (LDV) reveals near-perfect kinematic accuracy, with a relative amplitude error of only 1.65%. Furthermore, at a 100 Hz high-frequency excitation, the system successfully resolves microscopic displacements (≈10 μm) without temporal aliasing—enabled not by violating sampling theory but by leveraging the high physical line rate of the line-scan sensor. This establishes a robust, non-contact, and computationally efficient paradigm for broadband, micro-amplitude vibration monitoring in industrial environments. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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26 pages, 6199 KB  

Open AccessArticle

WeatherMAR: Complementary Masking of Paired Tokens for Adverse-Weather Image Restoration

by Junyuan Ma, Qunbo Lv and Zheng Tan

J. Imaging 2026, 12(4), 154; https://doi.org/10.3390/jimaging12040154 - 2 Apr 2026

Viewed by 777

Abstract

Image restoration under adverse weather conditions has attracted increasing attention because of its importance for both human perception and downstream vision applications. Existing methods, however, are often designed for a single degradation type. We present WeatherMAR, a multi-weather restoration framework that formulates [...] Read more.

Image restoration under adverse weather conditions has attracted increasing attention because of its importance for both human perception and downstream vision applications. Existing methods, however, are often designed for a single degradation type. We present WeatherMAR, a multi-weather restoration framework that formulates adverse-weather restoration as a paired-domain completion problem in a shared continuous token space. Specifically, WeatherMAR concatenates degraded and clean token sequences into a joint paired-domain sequence and performs restoration through masked autoregressive modeling, in which self-attention enables direct cross-domain interaction. To strengthen conditional learning while avoiding trivial paired correspondences, we introduce complementary bidirectional masking together with an optional reverse objective used only during training to encourage degradation-aware representations. WeatherMAR further employs a conditional diffusion objective for continuous token prediction and adopts a progress-to-step schedule to improve inference efficiency. Extensive experiments on standard multi-weather benchmarks, including Snow100K, Outdoor-Rain, and RainDrop, show that WeatherMAR achieves the best PSNR/SSIM on Snow100K-S (38.14/0.9684), the best SSIM on Outdoor-Rain (0.9396), and the best PSNR on Snow100K-L (32.58) and RainDrop (33.12). These results demonstrate that paired-domain token completion provides an effective solution for adverse-weather restoration. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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24 pages, 3448 KB  

Open AccessArticle

Gaussian-Guided Stage-Aware Deformable FPN with Coarse-to-Fine Unit-Circle Resolver for Oriented SAR Ship Detection

by Liangjie Meng, Qingle Guo, Danxia Li, Jinrong He and Zhixin Li

Remote Sens. 2026, 18(7), 1019; https://doi.org/10.3390/rs18071019 - 29 Mar 2026

Viewed by 450

Abstract

Synthetic Aperture Radar (SAR) enables all-weather maritime surveillance, yet ship-oriented bounding box (OBB) detection remains challenging in complex scenes. Strong sea clutter and dense harbor scatterers often mask the slender characteristics of ships as well as the weak responses of small ships. Meanwhile, [...] Read more.

Synthetic Aperture Radar (SAR) enables all-weather maritime surveillance, yet ship-oriented bounding box (OBB) detection remains challenging in complex scenes. Strong sea clutter and dense harbor scatterers often mask the slender characteristics of ships as well as the weak responses of small ships. Meanwhile, the periodicity of angle parameterization introduces regression discontinuities, and near-symmetric, bright-scatterer-dominated signatures further cause heading ambiguity, undermining the stability of orientation prediction. Moreover, in most detectors, multi-scale feature fusion and angle estimation lack explicit coordination, and rotated-box localization performance is often jointly affected by feature degradation and unstable orientation prediction. To this end, we propose a unified framework that simultaneously strengthens multi-scale representations and stabilizes orientation modeling. Specifically, we design a Gaussian-Guided Stage-Aware Deformable Feature Pyramid Network (GSDFPN) and a Coarse-to-Fine Unit-Circle Resolver (CF-UCR). GSDFPN enhances multi-scale fusion with two plug-in components: (i) a Gaussian-guided High-level Semantic Refinement Module (GHSRM) that suppresses clutter-dominated semantics while strengthening ship-responsive cues, and (ii) a Stage-aware Deformable Fusion Module (SDFM) for low-level features, which disentangles channels into a geometry-preserving spatial stream and a clutter-resistant semantic stream, and couples them via deformable interaction with bidirectional cross-stream gating to better capture the inherent slender characteristics of ships and localize small ships. For orientation, CF-UCR decomposes angle prediction into direction-cluster classification and intra-cluster residual regression on the unit circle, effectively mitigating periodicity-induced discontinuities and stabilizing rotated-box estimation. On SSDD+ and RSDD, our method achieves AP/AP₅₀/AP₇₅ of 0.5390/0.9345/0.4529 and 0.4895/0.9210/0.4712, respectively, while reaching AP_s75/AP_m75/AP_l75 of 0.5614/0.8300/0.8392 and 0.4986/0.8163/0.8934, evidencing strong rotated-box localization across target scales in complex maritime scenes. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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21 pages, 4335 KB  

Open AccessArticle

Real-Time Small UAV Detection in Complex Airspace Using YOLOv11 with Residual Attention and High-Resolution Feature Enhancement

by Chuang Han, Md Redwan Ullah, Amrul Kayes, Khalid Hasan, Md Abdur Rouf, Md Rakib Hasan, Shen Tao, Guo Gengli and Mohammad Masum Billah

J. Imaging 2026, 12(3), 140; https://doi.org/10.3390/jimaging12030140 - 20 Mar 2026

Viewed by 1262

Abstract

Detecting small unmanned aerial vehicles (UAVs) in complex airspace presents significant challenges due to their minimal pixel footprint, resemblance to birds, and frequent occlusion. To address these issues, we propose YOLOv11-ResCBAM, a novel real-time detection framework that integrates a Residual Convolutional Block Attention [...] Read more.

Detecting small unmanned aerial vehicles (UAVs) in complex airspace presents significant challenges due to their minimal pixel footprint, resemblance to birds, and frequent occlusion. To address these issues, we propose YOLOv11-ResCBAM, a novel real-time detection framework that integrates a Residual Convolutional Block Attention Module (ResCBAM) and a high-resolution P2 detection head into the YOLOv11 architecture. ResCBAM enhances channel and spatial feature refinement while preserving original feature contexts through residual connections, and the P2 head maintains fine spatial details crucial for small-object localization. Evaluated on a custom dataset of 4917 images (11,733 after augmentation) across three classes (drone, bird, airplane), our model achieves a mean average precision at the 0.5–0.95 IoU threshold (mAP@0.5–0.95) of 0.845, representing a 7.9% improvement over the baseline YOLOv11n, while maintaining real-time inference at 50.51 FPS. Cross-dataset validation on VisDrone2019-DET and UAVDT benchmarks demonstrates promising generalization trends. This work demonstrates the effectiveness of the proposed approach for UAV surveillance systems, balancing detection accuracy with computational efficiency for deployment in security-critical environments. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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23 pages, 2010 KB  

Open AccessArticle

Visibility-Prior Guided Dual-Stream Mixture-of-Experts for Robust Facial Expression Recognition Under Complex Occlusions

by Siyuan Ma, Long Liu, Mingzhi Cheng, Peijun Qin, Zixuan Han, Cui Chen, Shizhao Yang and Hongjuan Wang

Electronics 2026, 15(6), 1230; https://doi.org/10.3390/electronics15061230 - 16 Mar 2026

Viewed by 556

Abstract

Facial occlusion induces sample-wise reliability shifts in facial expression recognition (FER), where the usefulness of global context and local discriminative cues varies dramatically with the amount of visible facial information. Existing occlusion-robust FER studies often evaluate under limited or homogeneous occlusion settings and [...] Read more.

Facial occlusion induces sample-wise reliability shifts in facial expression recognition (FER), where the usefulness of global context and local discriminative cues varies dramatically with the amount of visible facial information. Existing occlusion-robust FER studies often evaluate under limited or homogeneous occlusion settings and commonly adopt static fusion strategies, which are insufficient for complex and heterogeneous real-world occlusions. In this work, we establish a rigorous occlusion robustness evaluation protocol by constructing a fixed offline test benchmark with diverse synthetic occlusion patterns (e.g., masks, sunglasses, texture blocks, and mixed occlusions) on top of public FER test splits. We further propose a Dual-Stream Adaptive Weighting Mixture-of-Experts framework (DS-AW-MoE) that fuses a global contextual expert and a local discriminative expert via an occlusion-aware weighting network. Crucially, we introduce a facial visibility assessment as a task-agnostic prior to explicitly regulate expert contributions, enabling dynamic re-allocation of model capacity according to input-dependent feature reliability. Extensive experiments on public datasets and the constructed occlusion benchmark demonstrate that DS-AW-MoE achieves more stable recognition under complex occlusions, characterized by a smaller and more consistent performance drop. To support reproducibility under dataset license constraints, we will release an anonymous, fully runnable repository containing the complete occlusion synthesis pipeline, evaluation protocol, and configuration files, allowing researchers to reproduce the benchmark after obtaining the original datasets. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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28 pages, 5420 KB  

Open AccessArticle

HEMS-RTDETR: A Lightweight Edge-Enhanced and Deformation-Aware Detector for Floating Debris in Complex Water Environments

by Yiwei Cui, Xinyi Jiang, Haiting Yu, Meizhen Lei and Jia Ren

Electronics 2026, 15(6), 1226; https://doi.org/10.3390/electronics15061226 - 15 Mar 2026

Viewed by 644

Abstract

Floating debris detection in complex aquatic environments holds significant importance for water resource protection and maritime safety monitoring. However, this task faces three core challenges: severe background interference leading to blurred target textures, significant non-rigid deformations, and the frequent loss of small targets [...] Read more.

Floating debris detection in complex aquatic environments holds significant importance for water resource protection and maritime safety monitoring. However, this task faces three core challenges: severe background interference leading to blurred target textures, significant non-rigid deformations, and the frequent loss of small targets at long distances. To address these issues, we propose a high-performance lightweight detection algorithm, termed High-Efficiency Edge-Aware Multi-Scale Real-Time Detection Transformer (HEMS-RTDETR), built upon the Real-Time Detection Transformer (RT-DETR) architecture. First, to suppress disturbances induced by water surface ripples and specular reflections, a Cross-Stage Partial Multi-Scale Edge Information Enhancement (CSP-MSEIE) module is introduced to reconstruct the backbone network. By removing computational redundancy while incorporating explicit edge enhancement, feature extraction capability and noise robustness for weak-texture targets are significantly improved. Second, to handle irregular debris morphology, a Deformable Attention Transformer (DAT) module is integrated, enabling adaptive attention focusing on geometrically deformed regions. Finally, an Efficient Multi-Scale Bidirectional Feature Pyramid Network (EMBSFPN) is constructed to enhance cross-scale semantic interaction and alleviate small-target signal loss. Experimental results demonstrate that, compared with RTDETR-r18, HEMS-RTDETR reduces parameters to 12.57 M, improves mAP@0.5 and mAP@0.5:0.95 by 2.44% and 3.05%, respectively, and maintains real-time inference at 93 FPS, indicating strong robustness and application potential in dynamic aquatic environments. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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23 pages, 14232 KB  

Open AccessArticle

A Dual-Branch Perception Network for High-Precision Oriented Object Detection in Remote Sensing

by Qi Wang and Wei Sun

Remote Sens. 2026, 18(5), 839; https://doi.org/10.3390/rs18050839 - 9 Mar 2026

Cited by 2 | Viewed by 802

Abstract

With the rapid evolution of remote sensing earth observation technology, high-resolution object detection is crucial in military and civilian domains but faces challenges from expansive views and complex backgrounds. Small objects are particularly challenging due to their low pixel coverage, poor textures, and [...] Read more.

With the rapid evolution of remote sensing earth observation technology, high-resolution object detection is crucial in military and civilian domains but faces challenges from expansive views and complex backgrounds. Small objects are particularly challenging due to their low pixel coverage, poor textures, and susceptibility to drastic illumination changes and background clutter. To address these problems, this paper proposes MDCA-YOLO for oriented object detection. A Dual-Branch Perception Module (DBPM) is designed utilizing a synergistic mechanism of large-kernel and strip convolutions to establish long-range dependencies, accurately capturing geometric features of tiny objects even in the absence of local details; Multi-Adaptive Selection Fusion (MASF) is proposed to address cross-scale feature loss by adaptively enhancing feature response while suppressing background noise; furthermore, a reconstructed decoupled detection head, CoordAttOBB, significantly improves angle regression accuracy while reducing complexity. Experimental results on the DIOR-R dataset show MDCA-YOLO surpasses YOLO11s, improving mAP50 and mAP50:95 by 2.5% and 2.7%, respectively, effectively proving the algorithm’s superiority in remote sensing tasks. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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31 pages, 11349 KB  

Open AccessArticle

Recognition, Localization and 3D Geometric Morphology Calculation of Microblind Holes in Complex Backgrounds Based on the Improved YOLOv11 Network and AVC Algorithm

by Chengfen Zhang, Dong Xia, Ruizhao Chen, Qunfeng Niu, Tao Wang and Li Wang

J. Imaging 2026, 12(3), 96; https://doi.org/10.3390/jimaging12030096 - 24 Feb 2026

Viewed by 578

Abstract

Microblind hole processing quality inspection, especially accurately identifying microblind hole contour features and precisely detecting 3D and morphological parameters, has always been challenging, especially for accurately identifying those of different sizes, depths, and contour features simultaneously. This poses a great challenge for identifying [...] Read more.

Microblind hole processing quality inspection, especially accurately identifying microblind hole contour features and precisely detecting 3D and morphological parameters, has always been challenging, especially for accurately identifying those of different sizes, depths, and contour features simultaneously. This poses a great challenge for identifying and localizing microblind hole contours based on machine vision and accurately calculating three-dimensional parameters. This study takes cigarette microblind holes (diameter of 0.1–0.2 mm, depth of approximately 35 µm) as the research object. It focuses on solving two major challenges: recognizing and localizing microblind hole contours in complex texture backgrounds and accurately calculating their 3D geometric morphology. An improved YOLOv11s model is proposed for microblind hole image multiobject detection with complex texture backgrounds to extract their features completely. An Area–Volume Computation (AVC) algorithm, which utilizes discrete integral estimation and curve-fitting principles, is also proposed for computing their surface area and volume. The experimental results show that the precision, recall, mAP@0.5, mAP@0.5:0.95, and prediction time of the improved YOLOv11 network are 0.915, 0.948, 0.925, 0.615, and 1.27 ms, respectively. The relative errors (REs) of the surface area and volume calculation of the microblind holes are 5.236% and 3.964%, respectively. The proposed method achieves microblind hole recognition, localization and 3D morphology calculation accuracy, meeting cigarette on-site inspection criteria. Additionally, a reference for detecting other similar objects in complex texture backgrounds and accurately calculating 3D tasks is provided. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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22 pages, 38551 KB  

Open AccessArticle

Tiny Object Detection via Normalized Gaussian Label Assignment and Multi-Scale Hybrid Attention

by Shihao Lin, Li Zhong, Si Chen and Da-Han Wang

Remote Sens. 2026, 18(3), 396; https://doi.org/10.3390/rs18030396 - 24 Jan 2026

Viewed by 1428

Abstract

The rapid development of Convolutional Neural Networks (CNNs) has markedly boosted the performance of object detection in remote sensing. Nevertheless, tiny objects typically account for an extremely small fraction of the total area in remote sensing images, rendering existing IoU-based or area-based evaluation [...] Read more.

The rapid development of Convolutional Neural Networks (CNNs) has markedly boosted the performance of object detection in remote sensing. Nevertheless, tiny objects typically account for an extremely small fraction of the total area in remote sensing images, rendering existing IoU-based or area-based evaluation metrics highly sensitive to minor pixel deviations. Meanwhile, classic detection models face inherent bottlenecks in efficiently mining discriminative features for tiny objects, leaving the task of tiny object detection in remote sensing images as an ongoing challenge in this field. To alleviate these issues, this paper proposes a tiny object detection method based on Normalized Gaussian Label Assignment and Multi-scale Hybrid Attention. Firstly, 2D Gaussian modeling is performed on the feature receptive field and the actual bounding box, using Normalized Bhattacharyya Distance for precise similarity measurement. Furthermore, a candidate sample quality ranking mechanism is constructed to select high-quality positive samples. Finally, a Multi-scale Hybrid Attention module is designed to enhance the discriminative feature extraction of tiny objects. The proposed method achieves 25.7% and 27.9% AP on the AI-TOD-v2 and VisDrone2019 datasets, respectively, significantly improving the detection capability of tiny objects in complex remote sensing scenarios. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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