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Deep Learning for Image Processing and Computer Vision
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Deep Learning for Image Processing and Computer Vision

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 10 November 2026 | Viewed by 13388

Editors

Dr. Honggang Chen

E-Mail Website
Guest Editor
College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
Interests: image/video restoration; image/video understanding; image/video coding
Special Issues, Collections and Topics in MDPI journals
Dr. Yun Liu

E-Mail Website
Guest Editor
College of Artificial Intelligence, Southwest University, Chongqing 400715, China
Interests: image enhancement; image restoration; deep learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Image processing, computer vision, and deep learning are pivotal in driving technological innovation, enabling machines to interpret, enhance, and analyze visual data with unprecedented precision. These advancements not only support critical applications in fields such as autonomous vehicles, healthcare, and smart cities, but also significantly accelerate the development of artificial intelligence, empowering systems to perform complex visual tasks autonomously and intelligently across diverse real-world environments.

This Special Issue aims to explore recent advances in the fields of image processing, computer vision, and deep learning.

This Special Issue aims to gather researchers and experts, sharing their innovative approaches, methodologies, and findings in addressing the challenges and advancing the state of the art in the fields of image processing, computer vision, and deep learning.

Dr. Honggang Chen
Dr. Yun Liu
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • image enhancement and restoration
  • visual recognition
  • image quality assessment
  • medical data processing
  • deep learning models
  • intelligent transportation systems

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Published Papers (7 papers)

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Research

31 pages, 16560 KB  
Article
HLCNet: An HVI-Guided Cross-Branch Network with Large-Small Convolutions for Practical Low-Light Image Enhancement
by Yuantao Zhang, Cairang Sanzhi, Dongcai Zhao, Zhicheng Dong, Jie Li and Bowen Liu
Appl. Sci. 2026, 16(15), 7678; https://doi.org/10.3390/app16157678 - 2 Aug 2026
Viewed by 251
Abstract
Images captured under practical low-light conditions typically suffer from insufficient brightness, color distortion, noise, and blur, and the enhancement process itself may further introduce overexposed highlights. This paper presents HLCNet, an HVI-guided Large-Small Convolutional Cross-Branch Network for low-light restoration. RGB inputs are transformed [...] Read more.
Images captured under practical low-light conditions typically suffer from insufficient brightness, color distortion, noise, and blur, and the enhancement process itself may further introduce overexposed highlights. This paper presents HLCNet, an HVI-guided Large-Small Convolutional Cross-Branch Network for low-light restoration. RGB inputs are transformed into the HVI space so that chromatic and intensity information can be enhanced in two complementary branches. Each branch applies LSConv to couple broad illumination context with local structural modeling, followed by SE channel recalibration and an LCA-based encoder–decoder, while a soft overexposure constraint suppresses excessive responses without hard clipping. To ensure a controlled comparison, CIDNet is reproduced in the same Tesla T4 environment, whereas the published CIDNet results and other previously reported values are explicitly marked as external references. On LOL-Blur, HLCNet raises the PSNR of the reproduced CIDNet baseline from 26.5438 dB to 27.6260 dB, increases the SSIM from 0.8839 to 0.8863, and reduces the LPIPS from 0.1224 to 0.1056. On LOL-v2 Real and Synthetic, it attains 23.843 dB and 25.991 dB PSNR, respectively. Ablation, sensitivity, qualitative, and perceptual color-space analyses indicate that HLCNet is particularly effective for low-light images containing blur and weak structural details. Full article
(This article belongs to the Special Issue Deep Learning for Image Processing and Computer Vision)
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33 pages, 22811 KB  
Article
Unified Multi-Level Grape Counting Framework with Low Annotation Cost for Natural Orchard Scenes
by Xiaofeng Xie, Jingying Yang and Zhaoli Shen
Appl. Sci. 2026, 16(15), 7500; https://doi.org/10.3390/app16157500 - 28 Jul 2026
Viewed by 296
Abstract
Accurate multi-level grape counting in natural orchard images provides useful visual information for image-based crop-load characterization and vineyard management. However, cluster overlap, occlusion, boundary adhesion, and illumination variation complicate berry localization and cluster-wise counting. Existing detection- and density-regression-based methods remain susceptible to missed [...] Read more.
Accurate multi-level grape counting in natural orchard images provides useful visual information for image-based crop-load characterization and vineyard management. However, cluster overlap, occlusion, boundary adhesion, and illumination variation complicate berry localization and cluster-wise counting. Existing detection- and density-regression-based methods remain susceptible to missed detections, false positives, and cross-cluster misassignment, whereas approaches relying on pixel- or instance-level supervision typically require costly annotations. To address these challenges, this paper proposes UniGC, a unified framework for multi-level grape counting. At the berry level, UniGC employs CSD-P2PNet, a P2PNet-based point-regression branch equipped with a Condition-Structure Decoupled Frequency Enhancement Block (CSD-Block), to improve the localization and counting of image-visible berries under complex illumination and occlusion using only berry point annotations. At the cluster level, a You Only Look Once (YOLO) detector predicts grape-cluster bounding boxes, which are used as prompts for the Segment Anything Model (SAM) to generate cluster instance masks. We further propose Boundary-Depth Guided Mask-Point Assignment (BDMPA), which assigns each predicted berry point to at most one grape-cluster instance and thereby produces cluster-wise berry counts. Selected key experiments on the mixed dataset organized from WGISD and GBISC were repeated using three random seeds. CSD-P2PNet achieved an MAE of 23.4407 ± 0.1577, an RMSE of 32.8898 ± 1.2682, and a point-localization F1 score of 0.8374 ± 0.0097. YOLO12-S achieved an F1 score of 0.8433 ± 0.0056 and an AP50 of 0.8789 ± 0.0080 for grape-cluster detection. For BpGC counting, UniGC obtained a matched-cluster MRD of 0.1288 ± 0.0043, a 1−FVU of 0.8589 ± 0.0098, and an all-cluster penalized mean relative deviation (ACP-MRD) of 0.5062 ± 0.0124. These results demonstrate that UniGC achieves competitive multi-level grape counting performance while requiring only point and bounding-box annotations for training. Full article
(This article belongs to the Special Issue Deep Learning for Image Processing and Computer Vision)
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23 pages, 3703 KB  
Article
Multi-Scale Context Fusion Method with Spatial Attention for Accurate Crop Disease Detection
by Yang Zhao, Liangchen Hu and Sen Xu
Appl. Sci. 2025, 15(17), 9341; https://doi.org/10.3390/app15179341 - 26 Aug 2025
Cited by 5 | Viewed by 1899
Abstract
Crop diseases not only severely affect crop quality but also lead to significant reductions in yield. To address the challenge of accurate crop disease detection in complex environments, we propose a novel detection method based on the YOLO11 model, termed YOLO-MSCM. To enhance [...] Read more.
Crop diseases not only severely affect crop quality but also lead to significant reductions in yield. To address the challenge of accurate crop disease detection in complex environments, we propose a novel detection method based on the YOLO11 model, termed YOLO-MSCM. To enhance the extraction of small-object features, we introduce the MCSA module, which improves the model’s spatial feature perception. Additionally, a SimRepHMS module is designed to leverage local contextual information and strengthen multi-scale feature fusion. To improve the model’s adaptability and generalization capability, we employ DynamicConv, which dynamically adjusts channel weights based on input-specific patterns. For more accurate bounding box localization, we incorporate the WIoUv3 loss function to optimize box regression. Experimental results demonstrate that YOLO-MSCM achieves improvements of 6.1% in precision, 9.1% in recall, 6.2% in mAP@50, and 3.7% in mAP@50:95 compared to the baseline YOLO11n model. Comparative evaluations with several mainstream and state-of-the-art models further validate the superior detection performance of YOLO-MSCM, offering a reliable and effective solution for accurate crop disease detection in complex scenarios. Full article
(This article belongs to the Special Issue Deep Learning for Image Processing and Computer Vision)
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29 pages, 4725 KB  
Article
Feature Fusion Using Deep Learning Algorithms in Image Classification for Security Purposes by Random Weight Network
by Mustafa Servet Kiran, Gokhan Seyfi, Merve Yilmaz, Engin Esme and Xizhao Wang
Appl. Sci. 2025, 15(16), 9053; https://doi.org/10.3390/app15169053 - 17 Aug 2025
Cited by 3 | Viewed by 2875
Abstract
Automated threat detection in X-ray security imagery is a critical yet challenging task, where conventional deep learning models often struggle with low accuracy and overfitting. This study addresses these limitations by introducing a novel framework based on feature fusion. The proposed method extracts [...] Read more.
Automated threat detection in X-ray security imagery is a critical yet challenging task, where conventional deep learning models often struggle with low accuracy and overfitting. This study addresses these limitations by introducing a novel framework based on feature fusion. The proposed method extracts features from multiple and diverse deep learning architectures and classifies them using a Random Weight Network (RWN), whose hyperparameters are optimized for maximum performance. The results show substantial improvements at each stage: while the best standalone deep learning model achieved a test accuracy of 83.55%, applying the RWN to a single feature set increased accuracy to 94.82%. Notably, the proposed feature fusion framework achieved a state-of-the-art test accuracy of 97.44%. These findings demonstrate that a modular approach combining multi-model feature fusion with an efficient classifier is a highly effective strategy for improving the accuracy and generalization capability of automated threat detection systems. Full article
(This article belongs to the Special Issue Deep Learning for Image Processing and Computer Vision)
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19 pages, 2622 KB  
Article
An Improved Lightweight Model for Defect Detection on Paths in Images
by Zhaoning Cui, Yuejia Xu, Xinyi Jin and Yu Li
Appl. Sci. 2025, 15(13), 7014; https://doi.org/10.3390/app15137014 - 21 Jun 2025
Cited by 3 | Viewed by 1981
Abstract
To address the challenges of detecting multi-scale road defects and the lack of lightweight designs in conventional detection models, we propose ACD-YOLOv8, an enhanced model based on YOLOv8s. Our model enhances baseline architecture by integrating three key components: a lightweight Cross-Scale Feature Fusion [...] Read more.
To address the challenges of detecting multi-scale road defects and the lack of lightweight designs in conventional detection models, we propose ACD-YOLOv8, an enhanced model based on YOLOv8s. Our model enhances baseline architecture by integrating three key components: a lightweight Cross-Scale Feature Fusion Module (CCFM), an ADown sampling operation, and a Dynamic Head (DyHead). Experimental results on the RDD2022 dataset demonstrate the superiority of our approach. Compared to the baseline YOLOv8s, ACD-YOLOv8 achieves a 0.9% increase in mAP@0.5 and a 1.6% increase in the more stringent mAP@0.5:0.95 metric. Simultaneously, the model’s parameter count is reduced by 3.72 million (a 33.3% reduction) and its size is reduced by 7.4 MB. This work provides a practical and scalable solution for deploying high-accuracy defect detection on resource-constrained mobile platforms, offering significant potential to enhance traffic safety and maintenance efficiency. Full article
(This article belongs to the Special Issue Deep Learning for Image Processing and Computer Vision)
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26 pages, 15489 KB  
Article
Weighted Feature Fusion Network Based on Multi-Level Supervision for Migratory Bird Counting in East Dongting Lake
by Haojie Zou, Hai Zhou, Guo Liu, Yingchun Kuang, Qiang Long and Haoyu Zhou
Appl. Sci. 2025, 15(5), 2317; https://doi.org/10.3390/app15052317 - 21 Feb 2025
Viewed by 1509
Abstract
East Dongting Lake is an important habitat for migratory birds. Accurately counting the number of migratory birds is crucial to assessing the health of the wetland ecological environment. Traditional manual observation and low-precision methods make it difficult to meet this demand. To this [...] Read more.
East Dongting Lake is an important habitat for migratory birds. Accurately counting the number of migratory birds is crucial to assessing the health of the wetland ecological environment. Traditional manual observation and low-precision methods make it difficult to meet this demand. To this end, this paper proposes a weighted feature fusion network based on multi-level supervision (MS-WFFNet) to count migratory birds. MS-WFFNet consists of three parts: an EEMA-VGG16 sub-network, a multi-source feature aggregation (MSFA) module, and a density map regression (DMR) module. Among them, the EEMA-VGG16 sub-network cross-injects enhanced efficient multi-scale attention (EEMA) into the truncated VGG16 structure. It uses multi-head attention to nonlinearly learn the relative importance of different positions in the same direction. With only a few parameters added, EEMA effectively suppresses the noise interference caused by a cluttered background. The MSFA module integrates a weighted mechanism to fully preserve low-level detail information and high-level semantic information. It achieves this by aggregating multi-source features and enhancing the expression of key features. The DMR module applies density map regression to the output of each path in the MSFA module. It ensures local consistency and spatial correlation among multiple regression results by using distributed supervision. In addition, this paper presents the migratory bird counting dataset DTH, collected using local monitoring equipment in East Dongting Lake. It is combined with other object counting datasets for extensive experiments, showcasing the proposed method’s excellent performance and generalization capability. Full article
(This article belongs to the Special Issue Deep Learning for Image Processing and Computer Vision)
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18 pages, 2656 KB  
Article
Multimodal Natural Disaster Scene Recognition with Integrated Large Model and Mamba
by Yuxuan Shao and Liwen Xu
Appl. Sci. 2025, 15(3), 1149; https://doi.org/10.3390/app15031149 - 23 Jan 2025
Cited by 5 | Viewed by 2868
Abstract
The accurate identification of natural disasters is crucial in ensuring effective post-disaster relief efforts. However, the existing models for disaster classification often incur high costs. To address this, we propose leveraging the most advanced pre-trained large language models, which offer superior generative and [...] Read more.
The accurate identification of natural disasters is crucial in ensuring effective post-disaster relief efforts. However, the existing models for disaster classification often incur high costs. To address this, we propose leveraging the most advanced pre-trained large language models, which offer superior generative and multimodal understanding capabilities. Using a question-answering approach, we extract textual descriptions and category prediction probabilities for disaster scenarios, which are then used as input to our proposed Mamba Multimodal Disaster Recognition Network (Mamba-MDRNet). This model integrates a large pre-trained model with the Mamba mechanism, enabling the selection of the most reliable modality information as a robust basis for scene classification. Extensive experiments demonstrate consistent performance improvements across various visual models with heterogeneous architectures. Notably, integrating EfficientNet within Mamba-MDRNet yielded 97.82% accuracy for natural scene classification, surpassing the performance of the CNN (91.75%), ViT (94.50%), and ResNet18 (97.25%). These results highlight the potential of multimodal models combining large models and the Mamba mechanism for disaster type prediction. Full article
(This article belongs to the Special Issue Deep Learning for Image Processing and Computer Vision)
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