Journal of Visual Communication and Image Representation最新文献

{"title":"Towards fast and effective low-light image enhancement via adaptive Gamma correction and detail refinement","authors":"Shaoping Xu,&nbsp;Qiyu Chen,&nbsp;Liang Peng,&nbsp;Hanyang Hu,&nbsp;Wuyong Tao","doi":"10.1016/j.jvcir.2026.104724","DOIUrl":"10.1016/j.jvcir.2026.104724","url":null,"abstract":"<div><div>Over the past decade, deep neural networks have significantly advanced low-light image enhancement (LLIE), achieving marked improvements in perceptual quality and robustness. However, these gains are increasingly accompanied by architectural complexity and computational inefficiency, widening the gap between enhancement performance and real-time applicability. This trade-off poses a critical challenge for time-sensitive scenarios requiring both high visual quality and efficient execution. To resolve the efficiency–quality trade-off in LLIE, we propose an ultra-lightweight framework comprising two computationally efficient modules: the adaptive Gamma correction module (AGCM) and the nonlinear refinement module (NRM). Specifically, the AGCM employs lightweight convolutions to generate spatially adaptive, pixel-wise Gamma maps that simultaneously mitigate global underexposure and suppress highlight overexposure, thereby preserving scene-specific luminance characteristics and ensuring visually natural global enhancement. Subsequently, the NRM employs two nonlinear transformation layers that logarithmically compress highlights and adaptively stretch shadows, effectively restoring local details without semantic distortion. Moreover, the first nonlinear transformation layer within the NRM incorporates residual connections to facilitate the capture and exploitation of subtle image features. Finally, the AGCM and NRM modules are jointly optimized using a hybrid loss function combining a reference-based fidelity term and no-reference perceptual metrics (i.e., local contrast, colorfulness, and exposure balance). Extensive experiments demonstrate that the proposed LLIE framework delivers performance comparable to state-of-the-art (SOTA) algorithms, while requiring only 8K parameters, achieving an optimal trade-off between enhancement quality and computational efficiency. This performance stems from our two-stage ultra-lightweight design: global illumination correction via pixel-adaptive Gamma adjustment, followed by detail-aware nonlinear refinement, all realized within a minimally parameterized architecture. As a result, the framework is uniquely suited for real-time deployment in resource-constrained environments.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"116 ","pages":"Article 104724"},"PeriodicalIF":3.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024845","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}

{"title":"Position-rotation graph and elevation partitioning strategy for traffic police gesture recognition","authors":"Jian He ,&nbsp;Rongqi Cao ,&nbsp;Cheng Zhang ,&nbsp;Suyu Wang","doi":"10.1016/j.jvcir.2025.104698","DOIUrl":"10.1016/j.jvcir.2025.104698","url":null,"abstract":"<div><div>Traffic police gesture recognition is important in autonomous driving. Most existing methods rely on extracting pixel-level features from RGB images, which lack interpretability due to the absence of explicit skeletal gesture features. Current deep learning approaches often fail to effectively model skeletal gesture information because they ignore the inherent connections between joint coordinate data and gesture semantics. Additionally, many methods fail to integrate multi-modal skeletal information (such as joint positions, rotations, and root orientation), limiting their ability to capture cross-modal correlations. Beyond methodological limitations, existing datasets often lack diversity in commanding directions, hindering fine-grained recognition of gestures intended for different traffic flows. To address these limitations, this paper presents the CTPGesture v2 dataset with Chinese traffic police gestures that command vehicles in four directions and proposes a skeleton-based graph convolution method for continuous gesture recognition. Specifically, a position-rotation graph (PR-Graph) is constructed with joint positions, rotations, and root rotations all in the same graph to enrich the graph’s representational power. An elevation partitioning strategy (EPS) is introduced to address the shortcutting issue of the conventional spatial configuration partitioning strategy (SCPS). Experiments demonstrate our method achieves 0.842 Jaccard score on CTPGesture v2 at 31.9 FPS, improving over previous works. The proposed PR-Graph and EPS establish a more descriptive graph for GCN and help capture cross-modality correlations during the graph convolution stages. Our code is available at <span><span>https://github.com/crq0528/RT-VIBT</span><svg><path></path></svg></span>. Our datasets are available at <span><span>https://github.com/crq0528/traffic-gesture-datasets</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104698"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925099","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}

{"title":"Deep low light image enhancement via Multi-Task Learning of Few Shot Exposure Imaging","authors":"Yi Wang,&nbsp;Haonan Su,&nbsp;Zhaolin Xiao,&nbsp;Haiyan Jin","doi":"10.1016/j.jvcir.2025.104693","DOIUrl":"10.1016/j.jvcir.2025.104693","url":null,"abstract":"<div><div>Deep low-light enhancement methods typically learn from long-exposure ground truths. While effective for dark scenes, this approach often causes overexposure in HDR scenarios and lacks adaptability to varying illumination levels. Therefore, we develop a deep low light image enhancement via Multi-task Learning of Few Shot Exposure Imaging (MLFSEI) which is formulated as Bayesian multi-task directed graphical model and predict the enhanced images by learning few-shot tasks comprising multi-exposure images and their corresponding exposure vectors. The proposed method predicts the enhanced image from the selected exposure vector and the learned latent variable among few tasks. The exposure vectors are defined as the characteristics of few shot exposure datasets containing mean, variance and contrast of images. Moreover, the multi order gradients are developed to constrain the structure and details from the ground truth. Experimental results demonstrate significant improvements, with average gains of 4.64 dB in PSNR and 0.071 in SSIM, along with an average reduction of 1.12 in NIQE across multiple benchmark datasets compared to state-of-the-art methods. Furthermore, the proposed method can be extended to accommodate multiple outputs with varying exposure levels among one model.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104693"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925085","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}

{"title":"DCMAE: A dual-branch contrastive masked autoencoder for 3D object detection","authors":"Xun Liu,&nbsp;Fei Wang,&nbsp;Zifeng Chen,&nbsp;Xingzhen Dong","doi":"10.1016/j.jvcir.2025.104675","DOIUrl":"10.1016/j.jvcir.2025.104675","url":null,"abstract":"<div><div>Learning robust local features from point clouds is crucial for 3D object detection; however, this task remains challenging due to the irregular structure, data sparsity, and lack of explicit topology in 3D scenes. Existing methods tend to focus either on local geometric or global semantic feature learning, making the joint optimization of both challenging. To address these problems, we propose a novel dual-branch local and global feature learning network for 3D object detection, namely DCMAE. It consists of a reconstruction branch and a contrastive branch. The reconstruction branch employs an online encoder to reconstruct the occluded region from visible points, focusing on capturing local geometric features. The contrastive branch uses a momentum encoder and patch-level contrastive learning to enhance the discriminability of local features by aligning them with the global context. In addition, we propose a Self- and Cross-Attention (SCA) decoder to alleviate masked-shape leakage from point coordinates by separating masked-token interactions while preserving semantic relationships with visible points. Experiments on SUN RGB-D and KITTI datasets demonstrate that DCMAE improves mAP by 1.3% and 2.6%, respectively. Compared with PiMAE, DCMAE achieves additional gains of 0.7% on SUN RGB-D and 1.1% on KITTI, showing superior performance in both indoor and outdoor scenarios.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104675"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145748547","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}

{"title":"Multi-layer graph constraint dictionary pair learning for image classification","authors":"Yulin Sun ,&nbsp;Guangming Shi ,&nbsp;Weisheng Dong ,&nbsp;Xuemei Xie","doi":"10.1016/j.jvcir.2025.104638","DOIUrl":"10.1016/j.jvcir.2025.104638","url":null,"abstract":"<div><div>Multi-layer dictionary learning (MDL) has demonstrated significantly improved performance for image classification. However, most of the existing MDL methods just overall shared dictionary learning architecture, which weakens the discrimination ability of the dictionaries. For this, we proposed a powerful framework called the Multi-layer Graph Constraint Dictionary Pair Learning (MGDPL). Our MGDPL integrates multi-layer dictionary pair learning, structure graph constraint, and discrimination sparse representations into a unified framework. First, the multi-layer structured dictionary learning mechanism is applied to dictionary pairs to enhance the discrimination performance by rebuilding the reconstruction error of the previous layer via the latter layer. Second, it subjects the structure graph constraint on the sub-sparse representations to ensure the discrimination capability of the near neighbor graph. Third, the multi-layer discriminant graph regularized constraint term can ensure high intra-class tightness and inter-class dispersion of dictionary atoms in reconstruction space. Extensive experiments show that MGDPL can achieve excellent performance over other state-of-the-arts.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"114 ","pages":"Article 104638"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468801","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}

{"title":"Aligning computational and human perceptions of image complexity: A dual-task framework for prediction and localization","authors":"Xiaoying Guo ,&nbsp;Liang Li ,&nbsp;Tao Yan ,&nbsp;Lu Wang ,&nbsp;Yuhua Qian","doi":"10.1016/j.jvcir.2025.104686","DOIUrl":"10.1016/j.jvcir.2025.104686","url":null,"abstract":"<div><div>Perceptual analysis of image complexity bridges affective computing and visual perception, providing deeper insights into visual content. Conventional approaches mainly focus on global complexity scoring, neglecting the localization of region-specific complexity cues crucial for human perception. To address these challenges, we propose ICCORN, a dual-task framework that predicts image complexity scores while detecting complexity regions simultaneously. By integrating modified ICNet and rank-consistent ordinal regression (CORN), ICCORN generates complexity activation maps that are highly consistent with eye movement heatmaps. Comprehensive cross-dataset evaluations on four datasets demonstrate that ICCORN’s robust performance across diverse image types, enhancing its applicability in visual complexity analysis. Additionally, we introduce ICEye, a novel eye-tracking dataset of 1200 images across eight semantic categories, annotated with gaze trajectories, heatmaps, and segmented regions. This dataset facilitates advanced research into computational modeling of human visual complexity perception. ICEye dataset is available at <span><span>https://github.com/gxyeagle19850102/ICEye</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104686"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797414","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}

{"title":"Deep color constancy via a color shift aware conditional diffusion model","authors":"Haonan Su ,&nbsp;Chenyu Wang ,&nbsp;Haiyan Jin ,&nbsp;Yuanlin Zhang ,&nbsp;Bin Wang ,&nbsp;Zhiyu Jiang","doi":"10.1016/j.jvcir.2025.104658","DOIUrl":"10.1016/j.jvcir.2025.104658","url":null,"abstract":"<div><div>Color constancy methods often struggle to generalize across different lighting conditions due to the complexity of lighting scenarios. Inspired by the impressive performance of diffusion models in image synthesis and restoration tasks, we propose a color shift aware conditional diffusion model operating in LCH color space. This model leverages the generative capabilities of diffusion models to embed color channels for color reconstruction, ensuring robust performance and high accuracy in color restoration. Additionally, to achieve more accurate color reconstruction during the model’s generative process, we further designed a color shift estimation module. This module effectively guides the color generation of the conditional diffusion model by capturing global deviations in the color channels. Through extensive experiments, it is shown that the proposed method achieves the best performance in the accuracy of color reproduction compared to other alternatives for single- and multi-illuminant scenes, exhibiting efficient generalization capabilities.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104658"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694270","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}

{"title":"Accelerating inter-frame prediction in Versatile Video Coding via deep learning-based mode selection","authors":"Xudong Zhang ,&nbsp;Jing Chen ,&nbsp;Huanqiang Zeng ,&nbsp;Wenjie Xiang ,&nbsp;Yuting Zuo","doi":"10.1016/j.jvcir.2025.104653","DOIUrl":"10.1016/j.jvcir.2025.104653","url":null,"abstract":"<div><div>Compared to its predecessor HEVC, VVC utilizes the Quad-Tree plus Multitype Tree (QTMT) structure for partitioning Coding Units (CU) and integrates a wider range of inter-frame prediction modes within its inter-frame coding framework. The incorporation of these innovative techniques enables VVC to achieve a substantial bitrate reduction of approximately 40% compared to HEVC. However, this efficiency boost is accompanied by a more than tenfold increase in encoding time. To accelerate the inter-frame prediction mode selection process, a FPMSN (Fast Prediction Mode Selection Network)-based method focusing on encoding acceleration during the non-partitioning mode testing phase is proposed in this paper. First, the execution results of the affine mode are collected as neural network input features. Next, FPMSN is proposed to extract critical information from multi-dimensional data and output the probabilities for each mode. Finally, multiple trade-off strategies are implemented to early terminate low-probability mode candidates.</div><div>Experimental results show that, under the Random Access (RA) configuration, the proposed method achieves a reduction in encoding time ranging from 3.22% to 19.3%, with a corresponding BDBR increase of only 0.12% to 1.363%, surpassing the performance of state-of-the-art methods.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104653"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694284","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}

{"title":"ADcFNet-deep learning based facial expression identification using FER vision transformer","authors":"M. Anand,&nbsp;S. Babu","doi":"10.1016/j.jvcir.2025.104637","DOIUrl":"10.1016/j.jvcir.2025.104637","url":null,"abstract":"<div><div>The stacked Gaussian blur edge detection filter (S-Gbed) is used for filtering. Dynamic Histogram equalization (DHE) is used to improve the contrast of an image. Then, a triple attention-assisted FER-vision transformer (T-FERViT) is used for feature extraction, and optimal features are selected using the Honey Badger chaotic optimization (HbcOa) algorithm. Finally, facial expressions are classified based on emotion using African vulture assisted Depth convolutional stacked Long Short-Term Memory (LSTM) Frame Attention network (ADcFNet). The African Vulture Optimization Algorithm is used to optimize the network’s loss function. Karolinska Directed Emotional Face dataset (KDEF), Face Expression Recognition-2013 (FER-2013) dataset, and facial emotion dataset are used to evaluate the ADcFNet model. Then, the overall performance of the proposed model is compared with other existing models to describe its superiority. The ADcFNet model attained 99.17%, 91.6%, and 95.9% accuracy in terms of KDEF, FER-2013, and facial emotion datasets, respectively.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"114 ","pages":"Article 104637"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571648","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}

{"title":"GAN semantics for personalized facial beauty synthesis and enhancement","authors":"Irina Lebedeva ,&nbsp;Fangli Ying ,&nbsp;Yi Guo ,&nbsp;Taihao Li","doi":"10.1016/j.jvcir.2025.104640","DOIUrl":"10.1016/j.jvcir.2025.104640","url":null,"abstract":"<div><div>Generative adversarial networks (GANs) whose popularity and scope of applications continue to grow, have already demonstrated impressive results in human face image processing. Face aging, completion, attribute transfer, and synthesis are not the only examples of the successful implementation of GANs. Although, beauty enhancement and face generation with conditioning on attractiveness level are also among the applications of GANs, it has been investigated only from the universal or generic point of view, and there are no studies addressed to the personalized aspect of these issues. In this work, this gap is filled and a generative framework that synthesizes a realistic human face that is based on an individual’s beauty preferences is introduced. To this end, StyleGAN’s properties and the capacities of semantic face manipulation in its latent space are studied and utilized. Beyond the face generation, the proposed framework is able to enhance a beauty level on a real face according to personal beauty preferences. Extensive experiments are conducted on two publicly available facial beauty datasets with different properties in terms of images and raters, SCUT-FBP5500 and multi-ethnic MEBeauty. The quantitative evaluations demonstrate the effectiveness of the proposed framework and its advantages compared to the state-of-the-art, while the qualitative evaluations also reveal and illustrate interesting social and cultural patterns in personal beauty preferences.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"114 ","pages":"Article 104640"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571726","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}