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29 pages, 11637 KB

Open AccessArticle

Scene Heatmap-Guided Adaptive Tiling and Dual-Model Collaboration-Based Object Detection in Ultra-Wide-Area Remote Sensing Images

by Fuwen Hu, Yeda Li, Jiayu Zhao and Chunping Min

Symmetry 2025, 17(12), 2158; https://doi.org/10.3390/sym17122158 - 15 Dec 2025

Viewed by 85

Abstract

This work addresses computational inefficiency in ultra-wide-area remote sensing image (RSI) object detection. Traditional homogeneous tiling strategies enforce computational symmetry by processing all image regions uniformly, ignoring the intrinsic spatial asymmetry of target distribution where target-dense coexist with vast target-sparse areas (e.g., deserts, farmlands), thereby wasting computational resources. To overcome symmetry mismatch, we propose a heat-guided adaptive blocking and dual-model collaboration (HAB-DMC) framework. First, a lightweight EfficientNetV2 classifies initial 1024 × 1024 tiles into semantic scenes (e.g., airports, forests). A target-scene relevance metric converts scene probabilities into a heatmap, identifying high-attention regions (HARs, e.g., airports) and low-attention regions (LARs, e.g., forests). HARs undergo fine-grained tiling (640 × 640 with 20% overlap) to preserve small targets, while LARs use coarse tiling (1024 × 1024) to minimize processing. Crucially, a dual-model strategy deploys: (1) a high-precision LSK-RTDETR-base detector (with Large Selective Kernel backbone) for HARs to capture multi-scale features, and (2) a streamlined LSK-RTDETR-lite detector for LARs to accelerate inference. Experiments show 23.9% faster inference on 30k-pixel images and reduction in invalid computations by 72.8% (from 50% to 13.6%) versus traditional methods, while maintaining competitive mAP (74.2%). The key innovation lies in repurposing heatmaps from localization tools to dynamic computation schedulers, enabling system-level efficiency for Ultra-Wide-Area RSIs. Full article

(This article belongs to the Topic Image Processing, Signal Processing and Their Applications)

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18 pages, 2411 KB

Open AccessArticle

AVD-YOLO: Active Vision-Driven Multi-Scale Feature Extraction for Enhanced Road Anomaly Detection

by Minhong Jin, Zhongjie Zhu, Renwei Tu, Ang Lv and Zhijing Yu

Information 2025, 16(12), 1064; https://doi.org/10.3390/info16121064 - 3 Dec 2025

Viewed by 277

Abstract

Deficiencies in road anomaly detection systems precipitate multifaceted risks, including elevated collision probabilities from unidentified hazards, compromised traffic flow efficiency, and exponential maintenance costs. Contemporary methods struggle with complex road environments, dynamic viewing perspectives, and limited datasets. We present AVD-YOLO, an enhanced YOLO variant that synergistically integrates Active Vision-Driven (AVD) multi-scale feature extraction with Position Modulated Attention (PMA) mechanisms. PMA addresses diminished target-background discriminability under variable illumination and weather conditions by capturing long range spatial dependencies, enhancing weak-feature target detection. The AVD technique mitigates missed detections caused by real-time viewing distance variations through adaptive multi-receptive field mechanisms, maintaining conceptual target fixation while dynamically adjusting feature scales. To address data scarcity, a comprehensive Multi-Class Road Anomaly Dataset (MCRAD) comprising 14,208 annotated images across nine anomaly categories is constructed. Experiments demonstrate that AVD-YOLO improves detection accuracy, achieving a 1.6% gain in mAP@0.5 and a 2.9% improvement in F1-score over baseline. These performance gains indicate both more precise localization of abnormal objects and a better balance between precision and recall, thereby enhancing the overall robustness of the detection model. Full article

(This article belongs to the Topic Image Processing, Signal Processing and Their Applications)

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13 pages, 5816 KB

Open AccessTechnical Note

Discretization of Digital Controllers Comprising Second-Order Notch Filters

by Alon Kuperman

Signals 2025, 6(4), 69; https://doi.org/10.3390/signals6040069 - 1 Dec 2025

Viewed by 186

Abstract

Second-order notch filters (NFs) with constant coefficients are often used as part of feedback controllers in grid-connected power conversion systems to prevent unwanted harmonic content polluting the closed control loops. In practice, the value of the mains frequency resides within a certain known range rather than remaining constant. Hence, the correct selection of NF coefficients is crucial for ensuring that the desired performance is maintained within the whole expected mains frequency range. Bilinear transformation (BLT) with notch frequency prewarping is often adopted to convert an NF from a continuous to a digital form. While accurately preserving the notch frequency location, the method reduces the filter bandwidth. As a remedy, BLT with both notch frequency and damping ratio prewarping may be employed. Nevertheless, some inaccuracy remains under low sampling-to-notch frequency ratios. This technical note demonstrates that the issue may be solved by prewarping the boundary values of the expected harmonic frequency range rather than the notch frequency and/or damping factor before applying the BLT. Simulation results accurately support the presented issue and proposed solution. Full article

(This article belongs to the Topic Image Processing, Signal Processing and Their Applications)

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24 pages, 43264 KB

Open AccessArticle

Document Image Verification Based on Paragraph Alignment and Subtle Change Detection

by Daoquan Li, Weifei Jia, Quanlin Yu and Zhaoxu Hu

Appl. Sci. 2025, 15(23), 12430; https://doi.org/10.3390/app152312430 - 23 Nov 2025

Viewed by 330

Abstract

The digitization of paper documents enables rapid sharing and long-term preservation of information, making it a widely adopted approach for efficient document storage and management across various domains. However, the recent advances in image editing software pose an increasing threat to the integrity of document images. Comparing the input with the corresponding reference document image is a direct and effective approach to verification. Nevertheless, this task is challenging due to two key factors, namely, the need for efficient retrieval of the reference document images and the difficulty of detecting subtle content changes under the print–scan (PS) distortions. To address these challenges, this work proposes a document image verification scheme based on paragraph alignment and subtle change detection. It first extracts paragraph structural features from both input and reference document images to achieve efficient image retrieval and accurate paragraph alignment. Based on the alignment results, the proposed scheme employs contrastive learning to reduce the effect of PS distortions in extracting features from the input and reference document images. Finally, an additional verification step is introduced that significantly reduces the false positive detection by addressing the feature misalignment within the extracted paragraphs. To evaluate the proposed scheme, extensive experiments were conducted on databases constructed from public datasets, and various benchmark methods were compared. Experimental results show that the proposed scheme outperforms benchmark methods, achieving an accuracy score of

0.963

. Full article

(This article belongs to the Topic Image Processing, Signal Processing and Their Applications)

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24 pages, 248126 KB

Open AccessArticle

Image Matching for UAV Geolocation: Classical and Deep Learning Approaches

by Fatih Baykal, Mehmet İrfan Gedik, Constantino Carlos Reyes-Aldasoro and Cefa Karabağ

J. Imaging 2025, 11(11), 409; https://doi.org/10.3390/jimaging11110409 - 12 Nov 2025

Viewed by 875

Abstract

Today, unmanned aerial vehicles (UAVs) are heavily dependent on Global Navigation Satellite Systems (GNSSs) for positioning and navigation. However, GNSS signals are vulnerable to jamming and spoofing attacks. This poses serious security risks, especially for military operations and critical civilian missions. In order to solve this problem, an image-based geolocation system has been developed that eliminates GNSS dependency. The proposed system estimates the geographical location of the UAV by matching the aerial images taken by the UAV with previously georeferenced high-resolution satellite images. For this purpose, common visual features were determined between satellite and UAV images and matching operations were carried out using methods based on the homography matrix. Thanks to image processing, a significant relationship has been established between the area where the UAV is located and the geographical coordinates, and reliable positioning is ensured even in cases where GNSS signals cannot be used. Within the scope of the study, traditional methods such as SIFT, AKAZE, and Multiple Template Matching were compared with learning-based methods including SuperPoint, SuperGlue, and LoFTR. The results showed that deep learning-based approaches can make successful matches, especially at high altitudes. Full article

(This article belongs to the Topic Image Processing, Signal Processing and Their Applications)

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20 pages, 11332 KB

Open AccessArticle

A Fast Nonlinear Sparse Model for Blind Image Deblurring

by Zirui Zhang, Zheng Guo, Zhenhua Xu, Huasong Chen, Chunyong Wang, Yang Song, Jiancheng Lai, Yunjing Ji and Zhenhua Li

J. Imaging 2025, 11(10), 327; https://doi.org/10.3390/jimaging11100327 - 23 Sep 2025

Viewed by 486

Abstract

Blind image deblurring, which requires simultaneous estimation of the latent image and blur kernel, constitutes a classic ill-posed problem. To address this, priors based on

L_{2}

L_{1}

, and

L_{p}

regularizations have been widely adopted. Based on this foundation [...] Read more.

Blind image deblurring, which requires simultaneous estimation of the latent image and blur kernel, constitutes a classic ill-posed problem. To address this, priors based on

L_{2}

L_{1}

, and

L_{p}

regularizations have been widely adopted. Based on this foundation and combining successful experiences of previous work, this paper introduces

L_{N}

regularization, a novel nonlinear sparse regularization combining the

L_{p}

and

L_{\infty}

norms via nonlinear coupling. Statistical probability analysis demonstrates that

L_{N}

regularization achieves stronger sparsity than traditional regularizations like

L_{2}

L_{1}

, and

L_{p}

regularizations. Furthermore, building upon the

L_{N}

regularization, we propose a novel nonlinear sparse model for blind image deblurring. To optimize the proposed

L_{N}

regularization, we introduce an Adaptive Generalized Soft-Thresholding (AGST) algorithm and further develop an efficient optimization strategy by integrating AGST with the Half-Quadratic Splitting (HQS) strategy. Extensive experiments conducted on synthetic datasets and real-world images demonstrate that the proposed nonlinear sparse model achieves superior deblurring performance while maintaining completive computational efficiency. Full article

(This article belongs to the Topic Image Processing, Signal Processing and Their Applications)

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17 pages, 8282 KB

Open AccessArticle

Research on ADPLL for High-Precision Phase Measurement

by Weilai Yao, Chenying Sun, Xindong Liang and Jianjun Jia

Symmetry 2025, 17(9), 1487; https://doi.org/10.3390/sym17091487 - 8 Sep 2025

Cited by 1 | Viewed by 766

Abstract

The inter-satellite laser interferometer, which functions as a high-performance displacement sensor, will be used in forthcoming space-based gravitational wave detection missions. The readout of these interferometers is typically performed by phasemeters based on all-digital phase-locked loops (ADPLLs) implemented in FPGAs. This paper proposes a feasible loop parameter design workflow and a comprehensive noise model, providing guidelines for designing and optimizing an ADPLL to meet specified bandwidth and precision requirements. The validity of our analysis is demonstrated through numerical performance measurements based on the modified digital splitting test. Full article

(This article belongs to the Topic Image Processing, Signal Processing and Their Applications)

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19 pages, 12806 KB

Open AccessArticle

A Vision Method for Detecting Citrus Separation Lines Using Line-Structured Light

by Qingcang Yu, Song Xue and Yang Zheng

J. Imaging 2025, 11(8), 265; https://doi.org/10.3390/jimaging11080265 - 8 Aug 2025

Viewed by 604

Abstract

The detection of citrus separation lines is a crucial step in the citrus processing industry. Inspired by the achievements of line-structured light technology in surface defect detection, this paper proposes a method for detecting citrus separation lines based on line-structured light. Firstly, a gamma-corrected Otsu method is employed to extract the laser stripe region from the image. Secondly, an improved skeleton extraction algorithm is employed to mitigate the bifurcation errors inherent in original skeleton extraction algorithms while simultaneously acquiring 3D point cloud data of the citrus surface. Finally, the least squares progressive iterative approximation algorithm is applied to approximate the ideal surface curve; subsequently, principal component analysis is used to derive the normals of this ideally fitted curve. The deviation between each point (along its corresponding normal direction) and the actual geometric characteristic curve is then adopted as a quantitative index for separation lines positioning. The average similarity between the extracted separation lines and the manually defined standard separation lines reaches 92.5%. In total, 95% of the points on the separation lines obtained by this method have an error of less than 4 pixels. Experimental results demonstrate that through quantitative deviation analysis of geometric features, automatic detection and positioning of the separation lines are achieved, satisfying the requirements of high precision and non-destructiveness for automatic citrus splitting. Full article

(This article belongs to the Topic Image Processing, Signal Processing and Their Applications)

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Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Information information	2.9	6.5	2010	18.6 Days	CHF 1800	Submit
Remote Sensing remotesensing	4.1	8.6	2009	24.9 Days	CHF 2700	Submit
Signals signals	2.6	4.6	2020	22.9 Days	CHF 1200	Submit
Symmetry symmetry	2.2	5.3	2009	17.1 Days	CHF 2400	Submit
Journal of Imaging jimaging	3.3	6.7	2015	15.3 Days	CHF 1800	Submit

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