Journal of Visual Communication and Image Representation最新文献

RQVR: A multi-exposure image fusion network that optimizes rendering quality and visual realism

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-02-10 DOI: 10.1016/j.jvcir.2025.104410

Xiaokang Liu , Enlong Wang , Huizi Man , Shihua Zhou , Yueping Wang

{"title":"RQVR: A multi-exposure image fusion network that optimizes rendering quality and visual realism","authors":"Xiaokang Liu , Enlong Wang , Huizi Man , Shihua Zhou , Yueping Wang","doi":"10.1016/j.jvcir.2025.104410","DOIUrl":"10.1016/j.jvcir.2025.104410","url":null,"abstract":"<div><div>Deep learning has made significant strides in multi-exposure image fusion in recent years. However, it is still challenging to maintain the integrity of texture details and illumination. This paper proposes a novel multi-exposure image fusion method to optimize Rendering Quality and Visual Realism (RQVR), addressing limitations in recovering details lost under extreme lighting conditions. The Contextual and Edge-aware Module (CAM) enhances image quality by balancing global features and local details, ensuring the texture details of fused images. To enhance the realism of visual effects, an Illumination Equalization Module (IEM) is designed to optimize light adjustment. Moreover, a fusion module (FM) is introduced to minimize information loss in the fused images. Comprehensive experiments conducted on two datasets demonstrate that our proposed method surpasses existing state-of-the-art techniques. The results show that our method not only attains substantial improvements in image quality but also outperforms most advanced techniques in terms of computational efficiency.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"107 ","pages":"Article 104410"},"PeriodicalIF":2.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing visibility in hazy conditions: A multimodal multispectral image dehazing approach

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-02-07 DOI: 10.1016/j.jvcir.2025.104407

Mohammad Mahdizadeh , Peng Ye , Shaoqing Zhao

{"title":"Enhancing visibility in hazy conditions: A multimodal multispectral image dehazing approach","authors":"Mohammad Mahdizadeh , Peng Ye , Shaoqing Zhao","doi":"10.1016/j.jvcir.2025.104407","DOIUrl":"10.1016/j.jvcir.2025.104407","url":null,"abstract":"<div><div>Improving visibility in hazy conditions is crucial for many image processing applications. Traditional single-image dehazing methods rely heavily on recoverable details from RGB images, limiting their effectiveness in dense haze. To overcome this, we propose a novel multimodal multispectral approach combining hazy RGB and Near-Infrared (NIR) images. First, an initial haze reduction enhances the saturation of the RGB image. Then, feature mapping networks process both the NIR and dehazed RGB images. The resulting feature maps are fused using a cross-modal fusion strategy and processed through convolutional layers to reconstruct a haze-free image. Finally, fusing the integrated dehazed image with the NIR image mitigates over/under exposedness and improves overall quality. Our method outperforms state-of-the-art techniques on the EPFL dataset, achieving notable improvements across four key metrics. Specifically, it demonstrates a significant enhancement of 0.1932 in the FADE metric, highlighting its superior performance in terms of haze reduction and image quality. The code and implementation details are available at <span><span>https://github.com/PaulMahdizadeh123/MultimodalDehazing</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"107 ","pages":"Article 104407"},"PeriodicalIF":2.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A two-step enhanced tensor denoising framework based on noise position prior and adaptive ring rank

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-02-07 DOI: 10.1016/j.jvcir.2025.104406

Boyuan Li , Yali Fan , Weidong Zhang , Yan Song

{"title":"A two-step enhanced tensor denoising framework based on noise position prior and adaptive ring rank","authors":"Boyuan Li , Yali Fan , Weidong Zhang , Yan Song","doi":"10.1016/j.jvcir.2025.104406","DOIUrl":"10.1016/j.jvcir.2025.104406","url":null,"abstract":"<div><div>Recently, low-rank tensor recovery has garnered significant attention. Its objective is to recover a clean tensor from an observation tensor that has been corrupted. However, existing methods typically do not exploit the prior information of the noise’s position, and methods based on tensor ring decomposition also require a preset rank. In this paper, we propose a framework that leverages this prior information to transform the denoising problem into a complementary one, ultimately achieving effective tensor denoising. This framework consists of two steps: first, we apply an efficient denoising method to obtain the noise prior and identify the noise’s positions; second, we treat these positions as missing values and perform tensor ring completion. In the completion problem, we propose a tensor ring completion model with an adaptive rank incremental strategy, effectively addressing the preset rank problem. Our framework is implemented using the alternating direction method of multipliers (ADMM). Our method has been demonstrated to be superior through extensive experiments conducted on both synthetic and real data.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"107 ","pages":"Article 104406"},"PeriodicalIF":2.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ACGC: Adaptive chrominance gamma correction for low-light image enhancement

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-02-05 DOI: 10.1016/j.jvcir.2025.104402

N. Severoglu, Y. Demir, N.H. Kaplan, S. Kucuk

引用次数: 0

Noise variances and regularization learning gradient descent network for image deconvolution

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-02-05 DOI: 10.1016/j.jvcir.2025.104391

Shengjiang Kong , Weiwei Wang , Yu Han , Xiangchu Feng

{"title":"Noise variances and regularization learning gradient descent network for image deconvolution","authors":"Shengjiang Kong , Weiwei Wang , Yu Han , Xiangchu Feng","doi":"10.1016/j.jvcir.2025.104391","DOIUrl":"10.1016/j.jvcir.2025.104391","url":null,"abstract":"<div><div>Existing image deblurring approaches usually assume uniform Additive White Gaussian Noise (AWGN). However, the noise in real-world images is generally non-uniform AWGN and exhibits variations across different images. This work presents a deep learning framework for image deblurring that addresses non-uniform AWGN. We introduce a novel data fitting term within a regularization framework to better handle noise variations. Using gradient descent algorithm, we learn the inverse covariance of the non-uniform AWGN, the gradient of the regularization term, and the gradient adjusting factor from data. To achieve this, we unroll the gradient descent iteration into an end-to-end trainable network, where, these components are parameterized by convolutional neural networks. The proposed model is called the noise variances and regularization learning gradient descent network (NRL-GDN). Its major advantage is that it can automatically deal with both uniform and non-uniform AWGN. Experimental results on synthetic and real-world images demonstrate its superiority over existing baselines.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"107 ","pages":"Article 104391"},"PeriodicalIF":2.6,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143339499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GroupRF: Panoptic Scene Graph Generation with group relation tokens

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-02-04 DOI: 10.1016/j.jvcir.2025.104405

Hongyun Wang , Jiachen Li , Xiang Xiang , Qing Xie , Yanchun Ma , Yongjian Liu

{"title":"GroupRF: Panoptic Scene Graph Generation with group relation tokens","authors":"Hongyun Wang , Jiachen Li , Xiang Xiang , Qing Xie , Yanchun Ma , Yongjian Liu","doi":"10.1016/j.jvcir.2025.104405","DOIUrl":"10.1016/j.jvcir.2025.104405","url":null,"abstract":"<div><div>Panoptic Scene Graph Generation (PSG) aims to predict a variety of relations between pairs of objects within an image, and indicate the objects by panoptic segmentation masks instead of bounding boxes. Existing PSG methods attempt to straightforwardly fuse the object tokens for relation prediction, thus failing to fully utilize the interaction between the pairwise objects. To address this problem, we propose a novel framework named <strong>Group R</strong>elation<strong>F</strong>ormer (GroupRF) to capture the fine-grained inter-dependency among all instances. Our method introduce a set of learnable tokens termed group rln tokens, which exploit fine-grained contextual interaction between object tokens with multiple attentive relations. In the process of relation prediction, we adopt multiple triplets to take advantage of the fine-grained interaction included in group rln tokens. We conduct comprehensive experiments on OpenPSG dataset, which show that our method outperforms the previous state-of-the-art method. Furthermore, we also show the effectiveness of our framework by ablation studies. Our code is available at <span><span>https://github.com/WHY-student/GroupRF</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"107 ","pages":"Article 104405"},"PeriodicalIF":2.6,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing low-light color image visibility with hybrid contrast and saturation modification using a saturation-aware map

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-02-01 DOI: 10.1016/j.jvcir.2025.104392

Sepideh Khormaeipour, Fatemeh Shakeri

{"title":"Enhancing low-light color image visibility with hybrid contrast and saturation modification using a saturation-aware map","authors":"Sepideh Khormaeipour, Fatemeh Shakeri","doi":"10.1016/j.jvcir.2025.104392","DOIUrl":"10.1016/j.jvcir.2025.104392","url":null,"abstract":"<div><div>In this paper, we present a two-stage technique for color image enhancement. In the first stage, we apply the well-established Histogram Equalization method to enhance the overall contrast of the image. This is followed by a local enhancement method to address the differences in average local contrast between the original and enhanced images. In the second stage, we introduce a novel weighted map within a variational framework to adjust the saturation of the contrast-enhanced image. This weighted map identifies regions that require saturation modification and enables a controllable level of adjustment. The map is then multiplied by a maximally saturated color image derived from the original image, and the result is merged with the contrast-enhanced image. Compared to the original low-light image, our method significantly improves image quality, structure, color preservation, and saturation. Additionally, numerical experiments demonstrate that the proposed method outperforms other enhancement techniques in both qualitative and quantitative evaluations.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"107 ","pages":"Article 104392"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scale-invariant mask-guided vehicle keypoint detection from a monocular image

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-02-01 DOI: 10.1016/j.jvcir.2025.104397

Sunpil Kim , Gang-Joon Yoon , Jinjoo Song , Sang Min Yoon

{"title":"Scale-invariant mask-guided vehicle keypoint detection from a monocular image","authors":"Sunpil Kim , Gang-Joon Yoon , Jinjoo Song , Sang Min Yoon","doi":"10.1016/j.jvcir.2025.104397","DOIUrl":"10.1016/j.jvcir.2025.104397","url":null,"abstract":"<div><div>Intelligent vehicle detection and localization are important for autonomous driving systems, particularly traffic scene understanding. Robust vision-based vehicle localization directly affects the accuracy of self-driving systems but remains challenging to implement reliably due to differences in vehicle sizes, illumination changes, background clutter, and partial occlusion. Bottom-up-based vehicle detection using vehicle keypoint localization efficiently provides semantic information for partial occlusion and complex poses. However, bottom-up-based approaches still struggle to handle robust heatmap estimation from vehicles with scale variations and background ambiguities. This paper addresses the problem of predicting multiple vehicle locations by learning semantic vehicle keypoints using a multi-resolution feature extractor, an offset regression branch, and a heatmap regression branch network. The proposed pipeline estimates the vehicle keypoint by effectively eliminating similar background features using a mask-guided heatmap regression branch and emphasizing scale-adaptive heatmap features in the network. Quantitative and qualitative analyses, including ablation tests, verify that the proposed method is universally applicable, unlike previous approaches.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"107 ","pages":"Article 104397"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Depth completion based on multi-scale spatial propagation and tensor decomposition

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-01-31 DOI: 10.1016/j.jvcir.2025.104394

Mingming Sun, Tao Li, Qing Liao, Minghui Zhou

引用次数: 0

Document forgery detection based on spatial-frequency and multi-scale feature network

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-01-31 DOI: 10.1016/j.jvcir.2025.104393

Li Li , Yu Bai , Shanqing Zhang , Mahmoud Emam

{"title":"Document forgery detection based on spatial-frequency and multi-scale feature network","authors":"Li Li , Yu Bai , Shanqing Zhang , Mahmoud Emam","doi":"10.1016/j.jvcir.2025.104393","DOIUrl":"10.1016/j.jvcir.2025.104393","url":null,"abstract":"<div><div>Passive image forgery detection is one of the main tasks for digital image forensics. Although it is easy to detect and localize forged regions with high accuracies from tampered images through utilizing the diversity and rich detail features of natural images, detecting tampered regions from a tampered textual document image (photographs) still presents many challenges. These challenges include poor detection results and difficulty of identifying the applied forgery type. In this paper, we propose a robust multi-category tampering detection algorithm based on spatial-frequency(SF) domain and multi-scale feature fusion network. First, we employ frequency domain transform and SF feature fusion strategy to strengthen the network’s ability to discriminate tampered document textures. Secondly, we combine HRNet, attention mechanism and a multi-supervision module to capture the features of the document images at different scales and improve forgery detection results. Furthermore, we design a multi-category detection head module to detect multiple types of forgeries that can improve the generalization ability of the proposed algorithm. Extensive experiments on a constructed dataset based on the public StaVer and SCUT-EnsExam datasets have been conducted. The experimental results show that the proposed algorithm improves F1 score of document images tampering detection by nearly 5.73%, and it’s not only able to localize the tampering location, but also accurately identify the applied tampering type.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"107 ","pages":"Article 104393"},"PeriodicalIF":2.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0