Signal Processing-Image Communication最新文献

{"title":"ZERRIN-Net: Adaptive low-light image enhancement using Retinex decomposition and noise extraction","authors":"Wenchao Li ,&nbsp;Shuyuan Wen ,&nbsp;Jinhao Zhu ,&nbsp;Qiaofeng Ou ,&nbsp;Yanchun Guo ,&nbsp;Jiabao Chen ,&nbsp;Bangshu Xiong","doi":"10.1016/j.image.2025.117345","DOIUrl":"10.1016/j.image.2025.117345","url":null,"abstract":"<div><div>Low-light image enhancement aims at correcting the exposure of images taken under underexposed conditions while removing image noise and restoring image details. Most of the previous low-light image enhancement algorithms used hand-made a priori denoising in the corrected component; however, due to the large amount of detail information of the image contained in the corrected component and the presence of some pseudo-noise, the final enhancement results obtained by these solutions do not have the original noise removed, and the image details appear blurred. To solve the above problems, we propose ZERRIN-Net, a zero-shot low-light enhancement method based on Retinex decomposition. First of all, we first design the original noise extraction network N-Net, which can adaptively extract the original noise of low-light images without losing the detailed information of the images. In addition, we propose the decomposition network RI-Net, which is based on the Retinex principle and utilizes a simple self-supervised mechanism to help decompose a low-light image into a reflection component and a light component. In this paper, we conduct extensive experiments on numerous datasets as well as advanced vision tasks such as face detection, target recognition, and instance segmentation. The experimental results show that the performance of our method is competitive with current state-of-the-art methods. The code is available at: <span><span>https://github.com/liwenchao0615/ZERRINNet</span><svg><path></path></svg></span></div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"138 ","pages":"Article 117345"},"PeriodicalIF":3.4,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147613","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":"Hyperspectral mixed noise removal using nonconvex low-rank and total generalized variation","authors":"Xinwu Liu","doi":"10.1016/j.image.2025.117344","DOIUrl":"10.1016/j.image.2025.117344","url":null,"abstract":"<div><div>To better preserve the structural features while removing the mixed noise in hyperspectral image (HSI), this paper presents a novel HSI denoising method based on nonconvex low-rank (NLR) and total generalized variation (TGV) minimization. The proposed NLRTGV solver closely incorporates the advantages of TGV regularization, nonconvex nuclear norm, and <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-norm. More specifically, the TGV regularizer, which maintains the spatial structure features, is adopted to eliminate Gaussian noise and prevent the staircase artifacts. The usage of nonconvex penalty is to explore the spectral low-rank properties, which contributes to suppress the sparse noise and preserve the major data components. Besides, we further employ the <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-norm regularization to detect the sparse noise that includes impulse noise, deadlines and stripes. Computationally, by combining the iteratively reweighted <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> algorithm, singular value shrinkage method and primal-dual framework, we construct in detail a modified alternating direction method of multipliers to solve the resulting optimization problem. Finally, as evidently demonstrated in both simulated and real-world HSI datasets experiments, our proposed approach shows the outstanding denoising performance in terms of mixed noise removal and detail features preservation, over the existing state-of-the-art competitors.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"138 ","pages":"Article 117344"},"PeriodicalIF":3.4,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147612","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":"Signal processing for haptic surface modeling: A review","authors":"Antonio Luigi Stefani ,&nbsp;Niccolò Bisagno ,&nbsp;Andrea Rosani ,&nbsp;Nicola Conci ,&nbsp;Francesco De Natale","doi":"10.1016/j.image.2025.117338","DOIUrl":"10.1016/j.image.2025.117338","url":null,"abstract":"<div><div>Haptic feedback has been integrated into Virtual and Augmented Reality, complementing acoustic and visual information and contributing to an all-round immersive experience in multiple fields, spanning from the medical domain to entertainment and gaming. Haptic technologies involve complex cross-disciplinary research that encompasses sensing, data representation, interactive rendering, perception, and quality of experience. The standard processing pipeline, consists of (I) sensing physical features in the real world using a transducer, (II) modeling and storing the collected information in some digital format, (III) communicating the information, and finally, (IV) rendering the haptic information through appropriate devices, thus producing a user experience (V) perceptually close to the original physical world. Among these areas, sensing, rendering and perception have been deeply investigated and are the subject of different comprehensive surveys available in the literature. Differently, research dealing with haptic surface modeling and data representation still lacks a comprehensive dissection. In this work, we aim at providing an overview on modeling and representation of haptic surfaces from a signal processing perspective, covering the aspects that lie in between haptic information acquisition on one side and rendering and perception on the other side. We analyze, categorize, and compare research papers that address the haptic surface modeling and data representation, pointing out existing gaps and possible research directions.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"138 ","pages":"Article 117338"},"PeriodicalIF":3.4,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131260","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":"Driver distraction detection based on adaptive tiny targets and lightweight networks","authors":"Shuangshuang Gu ,&nbsp;Bin Wen ,&nbsp;Shiyao Chen ,&nbsp;Yuanyuan Li ,&nbsp;Guanqiu Qi ,&nbsp;Linhong Shuai ,&nbsp;Zhiqin Zhu","doi":"10.1016/j.image.2025.117342","DOIUrl":"10.1016/j.image.2025.117342","url":null,"abstract":"<div><div>Driver distraction detection is critical to reducing road traffic accidents and increasing the efficiency of advanced driver assistance systems. Real-time lightweight models are especially important for in-vehicle devices with limited computing resources. However, most existing methods focus on designing lighter network architectures and ignore the performance loss when detecting tiny targets. In order to realize the collaborative optimization of tiny target detection accuracy and network lightweight, a driver distraction detection method ATD<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>Net based on adaptive tiny target detection and lightweight networks is proposed. This method aims to reduce model complexity while fully capturing target features for accurate detection. ATD<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>Net consists of three core modules, Channel Reconstruction Perception Module (CRPM), Dynamic Spatial Self-locking Module (DSSM) and Structural Feedback Optimization Module (SFOM). CRPM reconfigures channels and reconstructs them into batch dimensions, uses parallel strategies to perceive interactive features between channels, and significantly enhances feature extraction capabilities. DSSM adopts dynamic locking and adaptive spatial selection mechanisms to capture multi-scale features while injecting adaptive spatial information. It effectively aggregates instance features and reduces the interference of conflicting information and background information, thereby improving the detection ability of tiny targets. SFOM uses dependency trees to model inter-layer relationships and integrate coupling parameters into groupings. It uses a sparse strategy to remove unimportant parameters, achieving lightweight modeling while balancing accuracy and speed. Experimental results show that ATD<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>Net is superior to the latest methods in driver distraction detection, showing excellent performance and good application prospects.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"138 ","pages":"Article 117342"},"PeriodicalIF":3.4,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090347","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":"Sparse modeling for image inpainting: A multi-scale morphological patch-based k-SVD and group-based PCA","authors":"Amit Soni Arya,&nbsp;Susanta Mukhopadhyay","doi":"10.1016/j.image.2025.117341","DOIUrl":"10.1016/j.image.2025.117341","url":null,"abstract":"<div><div>Image inpainting, a crucial task in image restoration, aims to reconstruct highly degraded images with missing pixels while preserving structural and textural integrity. Traditional patch-based and group-based sparse representation methods often struggle with visual artifacts and over-smoothing, limiting their effectiveness. To address these challenges, we propose a novel multi-scale morphological patch-based and group-based sparse representation learning approach for image inpainting. Our method enhances image inpainting by integrating morphological patch-based sparse representation (M-PSR) learning using k-singular value decomposition (k-SVD) and group-based sparse representation using principal component analysis (PCA) to construct adaptive dictionaries for improved reconstruction accuracy. Additionally, we employ the alternating direction method of multipliers (ADMM) to optimize the integration of morphological patch and group based sparse representations, enhancing restoration quality. Extensive experiments on various degraded images demonstrate that our approach outperforms state-of-the-art methods in terms of the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM). The proposed method effectively reconstructs images corrupted by missing pixels, scratches, and text inlays, achieving superior structural coherence and perceptual quality. This work contributes a robust and efficient solution for image inpainting, offering significant advances in sparse modeling and morphological image processing.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"138 ","pages":"Article 117341"},"PeriodicalIF":3.4,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070742","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":"An algorithm for voxelised solids representation using chain codes","authors":"Blaž Repnik ,&nbsp;Libor Váša ,&nbsp;Borut Žalik","doi":"10.1016/j.image.2025.117340","DOIUrl":"10.1016/j.image.2025.117340","url":null,"abstract":"<div><div>The paper introduces a new method to describe the surfaces of voxelised solids. It operates in three stages: a hierarchical linked list of chain code sequences is created first; the linked lists are pruned; and, finally, the content of the data structure is stored. The method uses chain codes from either a three- or nine-symbols alphabet. In the first case, two chain code symbols are needed to access the next face, while, in the second case, this is done by one symbol. The pair of chain codes from the three-symbols alphabet, or the individual symbol from the nine-symbols alphabet are considered as tokens. The sets of tokens are, in both cases, extended by two tokens, indicating the beginning and ending of the list. The method processes solids of any shape, including those containing holes, cavities, or multiple components existing in the same voxel space. Edge-connectivity is permitted. The method was compared against the method proposed by Lemus et al., which is designed for solids without holes. Although supporting a broader set of voxelised solids, the proposed method generates sequences of tokens that are, on average, up to 10% shorter. Since the information entropy of the sequences of tokens produced by the proposed method is also smaller, the obtained sequences are more compressible, as confirmed by applying <strong>gzip</strong> and <strong>bzip2</strong> data compressors.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"138 ","pages":"Article 117340"},"PeriodicalIF":3.4,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125382","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":"Facial expression transformation for anime-style image based on decoder control and attention mask","authors":"Xinhao Rao ,&nbsp;Weidong Min ,&nbsp;Ziyang Deng ,&nbsp;Mengxue Liu","doi":"10.1016/j.image.2025.117343","DOIUrl":"10.1016/j.image.2025.117343","url":null,"abstract":"<div><div>Human facial expression transformation has been extensively studied using Generative Adversarial Networks (GANs) recently. GANs have also shown successful attempts in transforming anime-style images. However, current methods for anime pictures fail to refine the expression control efficiently, leading to control effects weaker than expected. Moreover, it remains challenging to maintain the original anime face identity information while transforming. To address these issues, we propose an expression transformation method for anime-style images. In order to enhance the control effect of discrete emoticon tags, a mapping network is proposed to map them to high-dimensional control information, which is then injected into the network multiple times during transformation. Additionally, for better maintaining the anime face identity information while transforming, an integrated attention mask mechanism is introduced to enable the network's expression control to focus on the expression-related features, while avoiding affecting the unrelated features. Finally, we conduct a large number of experiments to verify the validity of the proposed method, and both quantitative and qualitative evaluations are carried out. The results demonstrate the superiority of our proposed method compared to existing methods based on multi-domain image-to-image translation.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"138 ","pages":"Article 117343"},"PeriodicalIF":3.4,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070741","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":"Multi-view contrastive learning for unsupervised 3D model retrieval and classification","authors":"Wenhui Li ,&nbsp;Zhenghao Fang ,&nbsp;Dan Song ,&nbsp;Weizhi Nie ,&nbsp;Xuanya Li ,&nbsp;An-An Liu","doi":"10.1016/j.image.2025.117333","DOIUrl":"10.1016/j.image.2025.117333","url":null,"abstract":"<div><div>Unsupervised 3D model retrieval and classification have attracted a lot of attention due to wide applications. Although much progress has been achieved, they remain challenging due to the lack of supervised information to optimize neural network learning. Existing unsupervised methods usually utilized clustering algorithms to generate pseudo labels for 3D models. However, the clustering algorithms cannot fully mine the multi-view structure information and misguide the unsupervised learning process due to the noise information. To cope with the above limitation, this paper proposes a Multi-View Contrastive Learning (MVCL) method, which fully takes advantage of multi-view structure information to optimize the neural network. Specifically, we propose a multi-view grouping scheme and multi-view contrastive learning scheme to mine the self-supervised information and learn discriminative feature representation. The multi-view grouping scheme divides the multiple views of each 3D model into two groups and minimizes the group-level difference, which facilitates exploring the internal characteristics of 3D structural information. To learn the relationships among multiple views in an unsupervised manner, we propose a two-stream asymmetrical framework including the main network and the subsidiary network to guarantee the discrimination of the learned feature. Extensive 3D model retrieval and classification experiments are conducted on two challenging datasets, demonstrating the superiority of this method.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"138 ","pages":"Article 117333"},"PeriodicalIF":3.4,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917401","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":"Adaptive structural compensation enhancement based on multi-scale illumination estimation","authors":"Yong Luo ,&nbsp;Qiuming Liu ,&nbsp;Xuejing Jiang ,&nbsp;Le Qin ,&nbsp;Zhenzhen Luo","doi":"10.1016/j.image.2025.117332","DOIUrl":"10.1016/j.image.2025.117332","url":null,"abstract":"<div><div>In real-world scenes, lighting conditions are often variable and uncontrollable, such as non-uniform lighting, low lighting, and overexposure. These uncontrolled lighting conditions can degrade image quality and visibility. However, the majority of existing image enhancement techniques are typically designed for specific lighting conditions. Consequently, when applied to images in uncontrolled lighting, these techniques are prone to result in insufficient visibility, distortion, overexposure, and even information loss. In this paper, to address the limitations of existing methods, we introduce an innovative and effective method for enhancing uncontrolled lighting images through adaptive structural compensation. Firstly, a joint filtering algorithm for illumination estimation is developed to effectively mitigate texture, edge and noise interference during illumination estimation. Secondly, we developed a multi-scale illumination estimation algorithm for the purpose of constructing a structural compensation map. This map is then used to control brightness compensation for different areas of the image. Finally, a two-stage compensation fusion strategy is designed to adaptively reconstruct the brightness distribution and effectively improve image visibility. Extensive experimental results indicate that our method outperforms some state-of-the-art approaches in improving the quality and visibility of images under uncontrolled lighting conditions.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"138 ","pages":"Article 117332"},"PeriodicalIF":3.4,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917400","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":"Information disentanglement for unsupervised domain adaptive Oracle Bone Inscriptions detection","authors":"Feng Gao ,&nbsp;Yongge Liu ,&nbsp;Deng Li ,&nbsp;Xu Chen ,&nbsp;Runhua Jiang ,&nbsp;Yahong Han","doi":"10.1016/j.image.2025.117334","DOIUrl":"10.1016/j.image.2025.117334","url":null,"abstract":"<div><div>The detection of Oracle Bone Inscriptions (OBIs) is the foundation of studying the OBIs via computer technology. Oracle bone inscription data includes rubbings, handwriting, and photos. Currently, most detection methods primarily focus on rubbings and rely on large-scale annotated datasets. However, it is necessary to detect oracle bone inscriptions on both handwriting and photo domains in practical applications. Additionally, annotating handwriting and photos is time-consuming and requires expert knowledge. An effective solution is to directly transfer the knowledge learned from the existing public dataset to the unlabeled target domain. However, the domain shift between domains heavily degrades the performance of this solution. To alleviate this problem and based on the characteristics of different domains of oracle bone, in this paper, we propose an information disentanglement method for the Unsupervised Domain Adaptive (UDA) OBIs detection to improve the detection performance of OBIs in both handwriting and photos. Specifically, we construct an image content encoder and a style encoder module to decouple the oracle bone image information. Then, a reconstruction decoder is constructed to reconstruct the source domain image guided by the target domain image information to reduce the shift between domains. To demonstrate the effectiveness of our method, we constructed an OBI detection benchmark that contains three domains: rubbing, handwriting, and photo. Extensive experiments verified the effectiveness and generality of our method on domain adaptive OBIs detection. Compared to other state-of-the-art UDAOD methods, our approach achieves an improvement of 0.5% and 0.6% in mAP for handwriting and photos, respectively.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"138 ","pages":"Article 117334"},"PeriodicalIF":3.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902465","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}