Signal Processing-Image Communication最新文献_第6页

Keypoint detection in Tai Chi Chuan Essence via Waist and Limbs Feature Separation 基于腰、四肢特征分离的太极拳精华关键点检测

IF 2.7 3区工程技术

Signal Processing-Image Communication Pub Date : 2026-02-01 Epub Date: 2025-11-25 DOI: 10.1016/j.image.2025.117439

Yi Yang , Hao Fu , Yunlong Lv , Wei Qian , Tian Wang

{"title":"Keypoint detection in Tai Chi Chuan Essence via Waist and Limbs Feature Separation","authors":"Yi Yang , Hao Fu , Yunlong Lv , Wei Qian , Tian Wang","doi":"10.1016/j.image.2025.117439","DOIUrl":"10.1016/j.image.2025.117439","url":null,"abstract":"<div><div>Tai Chi Chuan (TCC) teaching process requires face-to-face interaction between learners and masters, which restrains the promotion of TCC since skilled masters are rare and manual evaluation is time-consuming. Skeleton-based action recognition models are effective for this issue, while the existing human skeletal structures and detection frameworks are inadequate for this task. To address these challenges, a novel human skeletal structure aligned with the essence of TCC is proposed, along with a dataset with 2400 annotated images covering fundamental TCC movements. Furthermore, a module named Waist and Limbs Feature Separation (WLFS) is proposed for structured modeling. Based on the spatiotemporal characteristics of TCC movements, the WLFS module explicitly separates keypoints into dynamic and static categories channel-wise. Subsequently, two exclusive GAUs are applied to the static and dynamic regions respectively. This strategy enables the network to learn the distinct features of the separated categories regions, and accelerates the convergence of the network weights during the training process. To preserve the fine-scale features of TCC keypoints in the downsampling, a Multi-Scale Feature Fusion (MSFF) module is integrated into WLFS, which fuses the different spatial resolution feature maps to enhance feature representation of the model at small scales. Experiments on the custom dataset (Tai Chi) and public datasets (MPII, COCO-WholeBody V1.0, Animal Pose, and AP-10K) demonstrate that the proposed method achieves competitive performance and good generalization ability.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"141 ","pages":"Article 117439"},"PeriodicalIF":2.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145625286","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}

引用次数: 0

Natural image stitching using depth maps 使用深度图进行自然图像拼接

IF 2.7 3区工程技术

Signal Processing-Image Communication Pub Date : 2026-02-01 Epub Date: 2025-11-25 DOI: 10.1016/j.image.2025.117438

Tianli Liao , Nan Li

{"title":"Natural image stitching using depth maps","authors":"Tianli Liao , Nan Li","doi":"10.1016/j.image.2025.117438","DOIUrl":"10.1016/j.image.2025.117438","url":null,"abstract":"<div><div>Natural image stitching aims to create a single, natural-looking mosaic from overlapped images that capture the same 3D scene from different viewing positions. Challenges inevitably arise when the scene is non-planar and captured by handheld cameras since parallax is non-negligible in such cases. In this paper, we propose a novel image stitching method using depth maps, which generates accurate alignment mosaics against parallax. Firstly, we construct a robust fitting method to filter out the outliers in feature matches and estimate the epipolar geometry between input images. Then, we utilize epipolar geometry to establish pixel-to-pixel correspondences between the input images and render the warped images using the proposed optimal warping. In the rendering stage, we introduce several modules to solve the mapping artifacts in the warping results and generate the final mosaic. Experimental results on three challenging datasets demonstrate that the depth maps of input images enable our method to provide much more accurate alignment in the overlapping region and view-consistent results in the non-overlapping region. We believe our method will continue to work under the rapid progress of monocular depth estimation. The source code is available at <span><span>https://github.com/tlliao/NIS_depths</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"141 ","pages":"Article 117438"},"PeriodicalIF":2.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145625384","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}

引用次数: 0

Contrast and clustering: Learning neighborhood pair representation for source-free domain adaptation 对比与聚类：学习无源域自适应的邻域对表示

IF 2.7 3区工程技术

Signal Processing-Image Communication Pub Date : 2026-01-01 Epub Date: 2025-10-24 DOI: 10.1016/j.image.2025.117429

Yuqi Chen , Xiangbin Zhu , Yonggang Li , Yingjian Li , Haojie Fang

{"title":"Contrast and clustering: Learning neighborhood pair representation for source-free domain adaptation","authors":"Yuqi Chen , Xiangbin Zhu , Yonggang Li , Yingjian Li , Haojie Fang","doi":"10.1016/j.image.2025.117429","DOIUrl":"10.1016/j.image.2025.117429","url":null,"abstract":"<div><div>Unsupervised domain adaptation aims to address the challenge of classifying data from unlabeled target domains by leveraging source data from different distributions. However, conventional methods often necessitate access to source data, raising concerns about data privacy. In this paper, we tackle a more practical yet challenging scenario where the source domain data is unavailable, and the target domain data remains unlabeled. To address the domain discrepancy problem, we propose a novel approach from the perspective of contrastive learning. Our key idea revolves around learning a domain-invariant feature by: (1) constructing abundant pairs for feature learning by utilizing neighboring samples. (2) Refining negative pairs pool reduces learning confusion; and (3) combining noise-contrastive theory simplifies the function effectively. Through careful ablation studies and extensive experiments on three common benchmarks, VisDA, Office-Home, and Office-31, we demonstrate the superiority of our method over other state-of-the-art works. Our proposed approach not only offers practicality by alleviating the requirement of source domain data but also achieves remarkable performance in handling domain adaptation challenges. The code is available at <span><span>https://github.com/yukilulu/CaC</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"140 ","pages":"Article 117429"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417513","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}

引用次数: 0

TransformAR: A light-weight transformer-based metric for Augmented Reality quality assessment TransformAR：用于增强现实质量评估的基于轻量级变压器的度量

IF 2.7 3区工程技术

Signal Processing-Image Communication Pub Date : 2026-01-01 Epub Date: 2025-11-12 DOI: 10.1016/j.image.2025.117437

Aymen Sekhri , Mohamed-Chaker Larabi , Seyed Ali Amirshahi

{"title":"TransformAR: A light-weight transformer-based metric for Augmented Reality quality assessment","authors":"Aymen Sekhri , Mohamed-Chaker Larabi , Seyed Ali Amirshahi","doi":"10.1016/j.image.2025.117437","DOIUrl":"10.1016/j.image.2025.117437","url":null,"abstract":"<div><div>As Augmented Reality (AR) technology continues to gain traction in various sectors, ensuring a superior user experience has become an essential challenge for both academic researchers and industry professionals. However, the task of automatically predicting the quality of AR images remains difficult due to several inherent challenges, particularly the issue of visual confusion arising from the overlap of virtual and real-world elements. This paper introduces transformAR, a novel and efficient transformer-based framework designed to objectively assess the quality of AR images. The proposed model uses pre-trained vision transformers to capture content features from AR images, calculates distance vectors to measure the impact of distortions, and employs cross-attention-based decoders to effectively model the perceptual qualities of the AR images. Additionally, the training framework uses regularization techniques and label smoothing-like method to reduce the risk of overfitting. Through comprehensive experiments, we demonstrate that transformAR outperforms existing state-of-the-art approaches, offering a more reliable and scalable solution for AR image quality assessment.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"140 ","pages":"Article 117437"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520110","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}

引用次数: 0

UIT-OpenViIC: An open-domain benchmark for evaluating image captioning in Vietnamese unit - openviic：一个用于评估越南语图像字幕的开放域基准

IF 2.7 3区工程技术

Signal Processing-Image Communication Pub Date : 2026-01-01 Epub Date: 2025-10-31 DOI: 10.1016/j.image.2025.117430

Doanh C. Bui , Nghia Hieu Nguyen , Khang Nguyen

{"title":"UIT-OpenViIC: An open-domain benchmark for evaluating image captioning in Vietnamese","authors":"Doanh C. Bui , Nghia Hieu Nguyen , Khang Nguyen","doi":"10.1016/j.image.2025.117430","DOIUrl":"10.1016/j.image.2025.117430","url":null,"abstract":"<div><div>Image captioning is one of the vision-language tasks that continues to attract interest from the research community worldwide in the 2020s. The MS-COCO Caption benchmark is commonly used to evaluate the performance of advanced captioning models, even though it was introduced in 2015. However, recent captioning models trained on the MS-COCO Caption dataset perform well only in English language patterns; they do not perform as effectively in describing contexts specific to Vietnam or in generating fluent Vietnamese captions. To contribute to the low-resources research community as in Vietnam, we introduce a novel image captioning dataset in Vietnamese, the <strong>Open</strong>-domain <strong>Vi</strong>etnamese <strong>I</strong>mage <strong>C</strong>aptioning dataset (UIT-OpenViIC). The introduced dataset includes complex scenes captured in Vietnam and manually annotated by Vietnamese under strict rules and supervision. In this paper, we present in more detail the dataset creation process. From preliminary analysis, we show that our dataset is challenging to recent state-of-the-art (SOTA) Transformer-based baselines, which performed well on the MS COCO dataset. Then, the modest results prove that UIT-OpenViIC has room to grow, which can be one of the standard benchmarks in Vietnamese for the research community to evaluate their captioning models. Furthermore, we present a CAMO approach that effectively enhances the image representation ability by a multi-level encoder output fusion mechanism, which helps improve the quality of generated captions compared to previous captioning models. In our experiments, we show that our dataset is more diverse and challenging than the MS-COCO caption dataset, as indicated by the significantly lower CIDEr scores on our testing set, ranging from 59.52 to 62.47 compared to MS-COCO. For the CAMO approach, experiments on UIT-OpenViIC show that when equipped with a captioning baseline model, it can improve performance by 0.8970 to 4.9167 CIDEr.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"140 ","pages":"Article 117430"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466331","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}

引用次数: 0

Multi-exposure image enhancement and YOLO integration for nighttime pedestrian detection 夜间行人检测的多曝光图像增强和YOLO集成

IF 2.7 3区工程技术

Signal Processing-Image Communication Pub Date : 2026-01-01 Epub Date: 2025-09-26 DOI: 10.1016/j.image.2025.117421

Xiaobiao Dai , Junbo Lan , Zhigang Chen , Botao Wang , Xue Wen

{"title":"Multi-exposure image enhancement and YOLO integration for nighttime pedestrian detection","authors":"Xiaobiao Dai , Junbo Lan , Zhigang Chen , Botao Wang , Xue Wen","doi":"10.1016/j.image.2025.117421","DOIUrl":"10.1016/j.image.2025.117421","url":null,"abstract":"<div><div>This paper presents DCE<em>x</em>YOLO, a novel method integrating multi-exposure image enhancement with YOLO object detection for real-time pedestrian detection in nighttime driving scenarios. To address the challenges of uneven illumination and low-light conditions in nighttime driving scenarios, we introduce an improved Zero-DCE++ algorithm to generate enhanced images at multiple exposure levels, which are then combined with the original image as input to the YOLO detector. The method significantly enhances the synergistic effect between image enhancement and object detection through the design of multi-task loss functions and a two-stage optimization strategy. Extensive experiments on multiple datasets demonstrate that DCE<em>x</em>YOLO achieves an optimal balance between detection performance and efficiency, significantly reducing the log-average miss rate (MR<sup>-2</sup>) compared to the YOLO baseline. Therefore, this research validates the potential of multi-exposure enhancement in object detection under complex illumination environments, providing an efficient and reliable solution for intelligent driving and traffic safety, while establishing a foundation for future optimization of detection technologies in complex scenarios.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"140 ","pages":"Article 117421"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270194","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}

引用次数: 0

A robust JPEG quantization step estimation method for image forensics 一种用于图像取证的稳健JPEG量化步长估计方法

IF 2.7 3区工程技术

Signal Processing-Image Communication Pub Date : 2026-01-01 Epub Date: 2025-09-15 DOI: 10.1016/j.image.2025.117402

Chothmal Kumawat , Vinod Pankajakshan

{"title":"A robust JPEG quantization step estimation method for image forensics","authors":"Chothmal Kumawat , Vinod Pankajakshan","doi":"10.1016/j.image.2025.117402","DOIUrl":"10.1016/j.image.2025.117402","url":null,"abstract":"<div><div>Estimating JPEG quantization step size from a JPEG image stored in a lossless format after the decompression (D-JPEG image) is a challenging problem in image forensics. The presence of forgery or additive noise in the D-JPEG image makes the quantization step estimation even more difficult. This paper proposes a novel quantization step estimation method robust to noise addition and forgery. First, we propose a statistical model for the subband DCT coefficients of forged and noisy D-JPEG images. We then show that the periodicity in the difference between the absolute values of rounded DCT coefficients in a subband of a D-JPEG image and those of the corresponding never-compressed image can be used for reliably estimating the JPEG quantization step. The proposed quantization step estimation method is based on this observation. Detailed experimental results reported in this paper demonstrate the robustness of the proposed method against noise addition and forgery. The experimental results also demonstrate that the quantization steps estimated using the proposed method can be used for localizing forgeries in D-JPEG images.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"140 ","pages":"Article 117402"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120363","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}

引用次数: 0

MGMSDNet: Multi gradient multi scale attention driven denoiser network MGMSDNet：多梯度多尺度注意力驱动去噪网络

IF 2.7 3区工程技术

Signal Processing-Image Communication Pub Date : 2026-01-01 Epub Date: 2025-10-27 DOI: 10.1016/j.image.2025.117426

Debashis Das, Suman Kumar Maji

{"title":"MGMSDNet: Multi gradient multi scale attention driven denoiser network","authors":"Debashis Das, Suman Kumar Maji","doi":"10.1016/j.image.2025.117426","DOIUrl":"10.1016/j.image.2025.117426","url":null,"abstract":"<div><div>Image denoising is essential in applications such as medical imaging, remote sensing, and photography. Despite advancements in deep learning, denoising models still face key limitations. Most state-of-the-art methods increase network depth to boost performance, leading to higher computational costs, complex training, and diminishing returns. Moreover, the role of gradient information and negative image features in denoising is often overlooked, limiting the ability to capture fine structures. Our observations reveal that excessively deep networks can reduce denoising performance by introducing redundancy and complicating feature extraction. To address this, we propose MGMSDNet, a Gradient-Guided Convolutional Neural Network (CNN) with attention mechanisms that balance denoising performance and computational efficiency. MGMSDNet introduces a unique attention framework that utilizes multidirectional gradients and negative image features separately, enhancing structural preservation and noise suppression. To the best of our knowledge, this study is the first to explore multidirectional gradients for image denoising literature. MGMSDNet surpasses state-of-the-art methods on benchmark datasets, confirmed by quantitative metrics and visual comparisons. Ablation studies highlight the effectiveness of individual network components. For more details and implementation, visit our GitHub repository: <span><span>MGMSDNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"140 ","pages":"Article 117426"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417514","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}

引用次数: 0

RotCLIP: Tuning CLIP with visual adapter and textual prompts for rotation robust remote sensing image classification RotCLIP：调整剪辑与视觉适配器和文本提示旋转鲁棒遥感图像分类

IF 2.7 3区工程技术

Signal Processing-Image Communication Pub Date : 2026-01-01 Epub Date: 2025-09-19 DOI: 10.1016/j.image.2025.117407

Tiecheng Song, Qi Liu, Anyong Qin, Yin Liu

{"title":"RotCLIP: Tuning CLIP with visual adapter and textual prompts for rotation robust remote sensing image classification","authors":"Tiecheng Song, Qi Liu, Anyong Qin, Yin Liu","doi":"10.1016/j.image.2025.117407","DOIUrl":"10.1016/j.image.2025.117407","url":null,"abstract":"<div><div>In recent years, Contrastive Language-Image Pretraining (CLIP) has achieved remarkable success in a range of visual tasks by aligning visual and textual features. However, it remains a challenge to improve the robustness of CLIP for rotated images, especially for remote sensing images (RSIs) where objects can present various orientations. In this paper, we propose a Rotation Robust CLIP model, termed RotCLIP, to achieve the rotation robust classification of RSIs with a visual adapter and dual textual prompts. Specifically, we first compute the original and rotated visual features through the image encoder of CLIP and the proposed Rotation Adapter (Rot-Adapter). Then, we explore dual textual prompts to compute the textual features which describe original and rotated visual features through the text encoder of CLIP. Based on this, we further build a rotation robust loss to limit the distance of the two visual features. Finally, by taking advantage of the powerful image-text alignment ability of CLIP, we build a global discriminative classification loss by combining the prediction results of both original and rotated image-text features. To verify the effect of our RotCLIP, we conduct experiments on three RSI datasets, including the EuroSAT dataset used for scene classification, and the NWPU-VHR-10 and RSOD datasets used for object classification. Experimental results show that the proposed RotCLIP improves the robustness of CLIP against image rotation, outperforming several state-of-the-art methods.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"140 ","pages":"Article 117407"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100067","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}

引用次数: 0

MTDNet: A crowd counting network based on a multiscale transformer and dilated convolution MTDNet：一个基于多尺度变压器和扩展卷积的人群计数网络

IF 2.7 3区工程技术

Signal Processing-Image Communication Pub Date : 2026-01-01 Epub Date: 2025-10-10 DOI: 10.1016/j.image.2025.117423

Chongle Peng , Qingbing Sang , Xiaojun Wu , Zhaohong Deng , Lixiong Liu

{"title":"MTDNet: A crowd counting network based on a multiscale transformer and dilated convolution","authors":"Chongle Peng , Qingbing Sang , Xiaojun Wu , Zhaohong Deng , Lixiong Liu","doi":"10.1016/j.image.2025.117423","DOIUrl":"10.1016/j.image.2025.117423","url":null,"abstract":"<div><div>Crowd counting methods based on Convolutional Neural Networks (CNNs) often struggle to model contextual features effectively due to their limited feature extraction scope. In contrast, the Transformer model, through its global self-attention mechanism, can consider information from all positions simultaneously, thereby modeling contextual relationships more comprehensively and enhancing the model's ability to understand sequences. Building on this, we propose a crowd counting network that leverages multi-scale features extracted by Transformer and dilated convolutions. Specifically, we utilize Vision Transformer to capture global information and integrate low-level detail features with high-level semantic features, enabling the model to effectively extract semantic features with global contextual information. Additionally, we design a multi-branch dilated convolution regression module, composed of dilated convolutions, to process feature maps generated during the encoding phase, which can effectively produce predicted density maps and yield accurate regression results. Furthermore, we introduce a novel loss function for our pipeline, which enhances the model's fitting capability and results in smoother density maps.</div><div>Extensive experiments on four public crowd counting datasets demonstrate that our method achieves state-of-the-art results on several benchmarks. Particularly on the ShanghaiTech dataset, MTDNet achieves the most advanced results, with the MSE improving by 2.6% compared to the next best model.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"140 ","pages":"Article 117423"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321617","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}

引用次数: 0