Signal Processing-Image Communication最新文献

{"title":"UIQA-MSST: Multi-Scale Staircase-Transformer Fusion for Underwater Image Quality Assessment","authors":"Tianhai Chen ,&nbsp;Xichen Yang ,&nbsp;Tianshu Wang ,&nbsp;Shun Zhu ,&nbsp;Yan Zhang ,&nbsp;Zhongyuan Mao ,&nbsp;Nengxin Li","doi":"10.1016/j.image.2026.117479","DOIUrl":"10.1016/j.image.2026.117479","url":null,"abstract":"<div><div>Underwater images play a crucial role in underwater exploration and resource development, but their quality often degrades in complex underwater scenarios. However, existing methods mainly focus on specific scenarios, and exhibit limited generalization ability when addressing complex underwater scenarios. Enhancing their applicability is therefore essential for accurate quality assessment of underwater images across diverse scenarios. This paper proposes an Underwater Image Quality Assessment (UIQA) method that combines the advantages of staircase network and Transformer, focusing on efficiently capturing and integrating image features at different scales. Initially, multi-scale feature extraction is performed to obtain information from images at various levels. Following this, a Staircase Feature (SF) module progressively integrates features from shallow to deep layers, achieving fusion of cross-scale information. Additionally, the Cross-Scale Transformer (CST) module effectively merges information from multiple scales using self-attention mechanisms. By concatenating the output features of both modules, the model gains an understanding of image content across global and local ranges. Subsequently, a regression module is utilized to generate quality scores. Finally, meta-learning optimizes the model’s learning process, enabling adaptation to new data for accurate image quality prediction across diverse scenarios. Experiments show superior accuracy and stability on underwater datasets, with additional tests on natural scenes that demonstrate broader applicability. Cross-dataset experiments validate the generalization capability of the proposed method. The source code will be made available at <span><span>https://github.com/dart-into/UIQA-MSST</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"142 ","pages":"Article 117479"},"PeriodicalIF":2.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CS-YOLO:A small object detection model based on YOLO for UAV aerial photography","authors":"Rui Fan ,&nbsp;Renhao Jiao ,&nbsp;Weigui Nan ,&nbsp;Haitao Meng ,&nbsp;Abin Jiang ,&nbsp;Xiaojia Yang ,&nbsp;Zhiqiang Zhao ,&nbsp;Jin Dang ,&nbsp;Zhixue Wang ,&nbsp;Yanshan Tian ,&nbsp;Baiying Dong ,&nbsp;Xiaowei He ,&nbsp;Xiaoli Luo","doi":"10.1016/j.image.2025.117460","DOIUrl":"10.1016/j.image.2025.117460","url":null,"abstract":"<div><div>With the rapid development of the UAV industry, the application of object detection technology based on UAV aerial images is becoming more and more extensive. However, the target of UAV aerial image is small, dense and disturbed by complex environment, which makes object detection face great challenges. In order to solve the problems of dense small targets and strong background interference in UAV aerial images, we propose a YOLO-based UAV aerial image detection model-Content-Conscious and Scale-Sensitive (CS-YOLO). Unlike existing YOLO-based approaches, our contribution lies in the joint design of Bottleneck Attention Module-cross-stage partial (BAM-CSP), Multi-Scale Pooling Attention Fusion Module (MPAFM) and Feature Difference Fusion Module (FDFM). The BAM-CSP module significantly enhances the small target feature response by integrating the channel attention mechanism at the bottleneck layer of the cross-stage partial network; the MPAFM module adopts a multi-scale pooling attention fusion architecture, which suppresses complex background interference through parallel pooling and enhances the background perception ability of small targets. The FDFM module captures the information changes during the sampling process through the feature difference fusion mechanism. The Gradient Adaptive-Efficient IoU (GA-EIoU) loss function is introduced to optimize bounding box regression performance by incorporating the EIoU gradient constraint weighting mechanism. Comparative experiments on the VisDrone2019 dataset, CS-YOLO achieves 22.6% mAP@50:95, which is 2.7% higher than YOLO11n; on the HazyDet dataset, CS-YOLO achieved 53.8% mAP@50:95, an increase of 2.8%. CS-YOLO also comprehensively surpasses the existing advanced methods in terms of recall rate and robustness. Meanwhile, we conducted ablation experiments to verify the gain effect of each module on the detection performance. The model effectively solves the technical problems such as dense small targets and strong environmental interference in UAV aerial images, and provides a high-precision, real-time and reliable detection scheme for complex tasks such as UAV inspection. The source code will be available at <span><span>https://github.com/unscfr/CS-YOLO</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"142 ","pages":"Article 117460"},"PeriodicalIF":2.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single object tracking based on Spatio-Temporal information","authors":"Lixin Wei ,&nbsp;Yun Luo ,&nbsp;Rongzhe Zhu ,&nbsp;Xin Li","doi":"10.1016/j.image.2025.117463","DOIUrl":"10.1016/j.image.2025.117463","url":null,"abstract":"<div><div>To address the challenge of tracking difficulties due to the absence of temporal dynamic information and background clutter interference caused by similar backgrounds, similar objects, target occlusion, and illumination changes during target tracking, this paper proposes a single object tracking algorithm based on spatio-temporal information (SST). The algorithm integrates a Temporal Adaptive Module (TAM) into the backbone network to generate a temporal kernel based on feature maps. This endows the network with the capability to model temporal dynamics, effectively utilizing the temporal relationships between frames to handle complex temporal dynamics such as changes in target motion states and environmental conditions. Additionally, to mitigate background clutter interference, the algorithm employs a Mixed Local Channel Attention (MLCA) mechanism, which captures channel and spatial information to focus the network on the target and reduce the impact of interfering information. The proposed algorithm was evaluated on OTB100, LaSOT, and NFS datasets. It achieved an AUC score of 70.7% on OTB, which represents a 1.3% improvement over the baseline tracker. On LaSOT and NFS datasets, it obtained AUC scores of 65.1% and 65.9%, respectively, showing improvements of 0.2% compared to the baseline tracker. The tracking speed exceeds 80fps, and the performance of the SST algorithm has been verified on self-made videos. The code is available at <span><span>https://github.com/xuexiaodemenggubao/sst</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"142 ","pages":"Article 117463"},"PeriodicalIF":2.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new baseline for edge detection: Make encoder–decoder great again","authors":"Yachuan Li ,&nbsp;Xavier Soria Poma ,&nbsp;Yongke Xi ,&nbsp;Guanlin Li ,&nbsp;Chaozhi Yang ,&nbsp;Qian Xiao ,&nbsp;Yun Bai ,&nbsp;Zongmin Li","doi":"10.1016/j.image.2026.117485","DOIUrl":"10.1016/j.image.2026.117485","url":null,"abstract":"<div><div>The performance of deep learning based edge detectors has surpassed human performance, but the huge computational cost and complex training strategies hinder their further development and application. In this paper, we alleviate these complexities with a vanilla encoder–decoder based detector. Firstly, we design a bilateral encoder to decouple the extraction process of spatial features and semantic features. As the spatial branch no longer guides the semantic branch, feature richness can be reduced, enabling a more compact model design. We propose a cascaded feature fusion decoder, where the spatial features are progressively refined by semantic features. The refined spatial features are the only basis for generating the edge map. The coarse original spatial features and semantic features are avoided from direct contact with the final result. So the noise in the spatial features and the location error in the semantic features can be suppressed in the generated edge map. The proposed New Baseline for Edge Detection (NBED) achieves superior performance consistently across multiple edge detection benchmarks, even compared with those methods with huge computational costs and complex training strategies. The ODS of NBED on BSDS500 is 0.838, achieving state-of-the-art performance. Our study highlights that high-quality features are key to modern edge detection, and encoder–decoder based detectors can achieve excellent performance without complex training or heavy computation. Furthermore, we take retinal vessel segmentation as an example to explore the application of NBED in downstream tasks. The code is available at <span><span>https://github.com/Li-yachuan/NBED</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"142 ","pages":"Article 117485"},"PeriodicalIF":2.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization model for sign language recognition using hybrid convolution networks","authors":"S. Venkatesh ,&nbsp;Pravin R. Kshirsagar ,&nbsp;R. Thiagarajan ,&nbsp;Tan Kuan Tak ,&nbsp;B. Sivaneasan","doi":"10.1016/j.image.2025.117444","DOIUrl":"10.1016/j.image.2025.117444","url":null,"abstract":"<div><div>Sign Language Gestures Recognition model seeks to provide effective communication for people by converting their sign language motions into spoken or written language, allowing them to connect with non-signers. The deep features are extracted using Vision Transformer-YOLOv5 (ViT-YOLOv5), this model is employed to extract the Regions of Interest (ROI) from the images to generate the first set of features as F1. Concurrently, Scale-Invariant Feature Transform (SIFT) is used to extract a second set of features as F2 from the same images. The two extracted features are fed into a Hybrid Convolution-based Adaptive EfficientB7 Network (HCA-EfB7N). In this network, F1 is processed using 1D convolution, while F2 is processed using 2D convolution to obtain the recognized outcome. By utilizing both 1D and 2D convolutions, this proposed model accurately identifies the class of hand gestures, leading to more accurate recognition. The parameters of the HCA-EffB7 network are optimized using the Fitness-based Archimedes Optimization Algorithm (FAOA). This hybrid approach recognizes complex hand gestures, particularly in the sign language translation system. The proposed approach’s effectiveness is validated by comparing its performance against several baseline systems, thereby confirming its superiority and robustness in recognizing sign language gestures.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"141 ","pages":"Article 117444"},"PeriodicalIF":2.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145748485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tri-modal fusion for dynamic hand gesture recognition: Integrating RGB, depth, and skeleton data","authors":"Reena Tripathi,&nbsp;Bindu Verma","doi":"10.1016/j.image.2025.117440","DOIUrl":"10.1016/j.image.2025.117440","url":null,"abstract":"<div><div>Computer vision applications continue to show a strong interest in dynamic hand gesture recognition because of its wide range of applications in automation, human–computer interaction, and other fields. There are several challenges in dynamic hand gesture recognition, such as occlusion and background clutter, which makes gesture tracking and classification difficult. To solve this, we use a fusion of multiple modality concepts in our proposed work and each modality has its advantages. The first modality utilizes RGB data. It gives spatial information that helps interpret the gesturing hand’s shape, texture, and color information. The second modality employs depth data, which records activity motion. The third modality incorporates skeleton data. The challenges of complex backgrounds and occlusion are resolved by using skeletal data. The features are extracted parallelly from all modalities using a pre-trained ResCLIP model. In sequence-to-sequence learning, the LSTM unit processes the generated feature vectors. At the feature level, the features from all three LSTM networks are concatenated before the fully connected (FC) layer and SoftMax function is employed to classify the gestures. The proposed model was applied to two benchmark datasets, demonstrating its effectiveness, the First-person hand action dataset (FPHA) and the Sheffield Kinect Gesture dataset (SKIG). The proposed work outperformed the state-of-the-art techniques on the FPHA and SKIG datasets, exhibiting competitive performance.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"141 ","pages":"Article 117440"},"PeriodicalIF":2.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145625287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of image aesthetics assessment as a positive-unlabelled problem","authors":"Luis Gonzalez-Naharro,&nbsp;M. Julia Flores,&nbsp;Jesus Martínez-Gómez,&nbsp;Jose M. Puerta","doi":"10.1016/j.image.2025.117441","DOIUrl":"10.1016/j.image.2025.117441","url":null,"abstract":"<div><div>Image aesthetics assessment (IAA) has been traditionally addressed as a supervised learning problem, where the goal is to accurately predict information related to user opinions, such as the mean opinion score, image ratings, or a binary quality label, usually crafted by using a mean score threshold to label images as highly or lowly aesthetic.</div><div>Supervised approaches fail to take into account the subjectiveness of this problem, as the idea of aesthetic pleasantness varies among different people and different cultures, thus making the labels extremely noisy. However, the existence of worldwide photographic contests, exhibitions and masters implies that, to a reasonable degree, there is a broader consensus about the quality of very high-quality images and photographs. Furthermore, labelling image data for IAA is a difficult process, as a large amount of non-trivial aesthetic judgements are required for obtaining a large-scale IAA dataset.</div><div>Therefore, in this work we analyse the potential of positive-unlabelled techniques for solving IAA. We propose techniques for building PU datasets from traditional IAA datasets and from available reference datasets of high-quality images, and test several well-known PU algorithms on these. Our results highlight the potential of PU approaches for IAA, as we obtain results close to the state-of-the-art with much smaller sets of labelled data: in experiments with only 5% of labelled in AVA, we reach accuracy levels only 0.03 points below NIMA, and we reach competent balanced accuracy levels in settings with a very limited amount of labelled data and with very simple models.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"141 ","pages":"Article 117441"},"PeriodicalIF":2.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey on video emotion recognition: Segmentation, classification, and explainable AI techniques","authors":"Sudhakar Hallur,&nbsp;Anil Gavade,&nbsp;Priyanka Gavade","doi":"10.1016/j.image.2025.117442","DOIUrl":"10.1016/j.image.2025.117442","url":null,"abstract":"<div><div>Emotion recognition from videos has become a pivotal domain in computer vision and affective computing, contributing to advancements in human–computer interaction, healthcare, security, and multimedia analysis. This survey systematically reviews 137 research papers that span segmentation, classification, and explainable artificial intelligence (XAI) techniques for video-based emotion recognition. The study categorizes works into probabilistic, clustering, deep learning, affective computing, fuzzy logic, genetic algorithms, hybrid, multimodal, and XAI-based approaches. Through a structured evaluation of datasets such as FER2013, CK+, RAVDESS, AffectNet, and EMOTIC, this review highlights how convolutional, recurrent, and transformer architectures, combined with multimodal fusion and attention mechanisms, have enhanced emotion detection accuracy to maximum in certain contexts. It also identifies key challenges including dataset bias, multimodal synchronization, interpretability, and computational complexity. The paper emphasizes the rising importance of XAI in bridging the gap between model transparency and human cognition, proposing that future research focus on explainable, context-aware, and ethically grounded frameworks for robust emotion understanding. By consolidating diverse research trajectories, this survey offers a unified perspective on current advancements, limitations, and future directions in video emotion analysis.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"141 ","pages":"Article 117442"},"PeriodicalIF":2.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rank-based transformation algorithm for image contrast adjustment","authors":"Cheng-Hui Chen,&nbsp;Torbjörn E.M. Nordling","doi":"10.1016/j.image.2025.117432","DOIUrl":"10.1016/j.image.2025.117432","url":null,"abstract":"<div><div>Performing proper image contrast adjustment without information loss is an art. Many adjustment methods are used. The default settings are often inappropriate for the image in question rendering a contrast adjustment depending on trial and error. We propose a simple method, rank-based transformation (RBT), for image contrast adjustment that requires no prior knowledge. This makes RBT an ideal first tool to apply for underexposed images. The RBT algorithm normalizes and equalizes all the intensity differences of the image over the full intensity range of the image data type, and thus assigns equal weight to all gradients. Even the state-of-the-art AI tool Cellpose visually benefits from RBT preprocessing. Our comparison of histogram normalization methods demonstrates the ability of RBT to bring out image features.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"141 ","pages":"Article 117432"},"PeriodicalIF":2.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145584161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MSTSGM: A multi-scale temporal–spatial guided model for image deblurring","authors":"Boyu Pei ,&nbsp;Kejun Long ,&nbsp;Zhibo Gao ,&nbsp;Jian Gu ,&nbsp;Shaofei Wang ,&nbsp;Xinhu Lu","doi":"10.1016/j.image.2025.117443","DOIUrl":"10.1016/j.image.2025.117443","url":null,"abstract":"<div><div>Image deblurring is a critical task in computer vision, essential for recovering sharp images from blurry ones often caused by motion blur or camera shake. Recent advancements in deep learning have introduced convolutional neural networks (CNNs) as a powerful alternative, enabling the learning of intricate mappings between blurry and sharp images. However, existing deep learning approaches still struggle with effectively capturing low-frequency information and maintaining robustness across diverse blur conditions, while high-frequency details are often inadequately restored due to their susceptibility to motion blur. This paper presents the Multi-Scale Temporal–Spatial Guided Model (MSTSGM), which integrates multi-scale feature decoupling (MSFD), temporal convolution networks (TCN), and edge attention guided reconstruction (EAGR) to enhance deblurring performance. The MSFD captures a wide range of details by decomposing images into multi-scale representations, while the TCN refines these features by modeling temporal dependencies in blur formation. The EAGR focuses on key edge features, effectively improving image clarity. Evaluated on benchmark datasets including GoPro, HIDE, and RealBlur, MSTSGM demonstrates competitive performance, achieving higher PSNR and SSIM metrics compared to state-of-the-art methods. Ablation studies validate the contribution of each component, highlighting the synergistic effects of multi-scale processing, temporal feature integration, and edge attention. Furthermore, MSTSGM’s application as a preprocessing step for object detection tasks illustrates its practical utility in enhancing the accuracy of downstream computer vision applications. MSTSGM provides a robust solution for advancing image deblurring and related tasks in the field. Source code is available for research purposes at <span><span>https://github.com/priplex/MSTSGM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"141 ","pages":"Article 117443"},"PeriodicalIF":2.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145625385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}