Journal of Electronic Imaging最新文献

Two-dimensional hierarchical rate control scheme for light field compression using Multi-View Extension of High-Efficiency Video Coding 基于多视点扩展的高效视频编码光场压缩二维分层速率控制方案

4区计算机科学

Journal of Electronic Imaging Pub Date : 2025-09-03 DOI: 10.1117/1.jei.34.5.051005

Ali Hassan, Waqas Ahmad, Mubeen Ghafoor, Kamran Qureshi, Roger Olsson, Mårten Sjöström

{"title":"Two-dimensional hierarchical rate control scheme for light field compression using Multi-View Extension of High-Efficiency Video Coding","authors":"Ali Hassan, Waqas Ahmad, Mubeen Ghafoor, Kamran Qureshi, Roger Olsson, Mårten Sjöström","doi":"10.1117/1.jei.34.5.051005","DOIUrl":"https://doi.org/10.1117/1.jei.34.5.051005","url":null,"abstract":"Light field (LF) is envisioned to form a base for many visual applications, given its ability to capture a scene's spatial and angular information. However, using LF comes at the cost of increased bit rate and storage requirements. This paper performs a comprehensive analysis of the rate control aspect of LF data compression and proposes an extension of a rate control scheme originally designed for video signals. A two-dimensional hierarchical rate control strategy is proposed explicitly for LF data to meet a defined target bit rate with high accuracy while maintaining a similar rate-distortion efficiency as that presented in our previous state-of-the-art LF data compression scheme. Instead of considering the target bit budget for each video signal individually, the proposed rate control scheme considers the overall bit budget for an entire LF data compression in the bits allocation. To demonstrate the efficiency of the proposed scheme, the existing rate control scheme implemented in reference MV-HEVC, as well as the state-of-the-art LF coding methods on JPEG Pleno common test conditions, are considered for evaluation. The experimental results exhibit, on average, a 2 dB improvement in PSNR while achieving the target bit rate with 4.5% better accuracy than the reference MV-HEVC rate control scheme. It is clear from the extensive analysis that the proposed scheme outperforms the JPEG Pleno HEVC anchor scheme for all JPEG Pleno CTC lenslet LFs, thus fully justifying its inclusion in the JPEG Pleno Light Field Coding standard. In the future, the deep learning-based rate control scheme will be investigated, along with intra-frame bit allocation, to tackle low bit rates.","PeriodicalId":54843,"journal":{"name":"Journal of Electronic Imaging","volume":"34 05","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147889477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ColorTransferLabV2: a software testbed for multi-modal color transfer, colorization, and style transfer ColorTransferLabV2：用于多模态颜色转移、着色和样式转移的软件测试平台

4区计算机科学

Journal of Electronic Imaging Pub Date : 2025-08-09 DOI: 10.1117/1.jei.34.5.051002

Herbert Potechius, Thomas Sikora, Sebastian Knorr

{"title":"ColorTransferLabV2: a software testbed for multi-modal color transfer, colorization, and style transfer","authors":"Herbert Potechius, Thomas Sikora, Sebastian Knorr","doi":"10.1117/1.jei.34.5.051002","DOIUrl":"https://doi.org/10.1117/1.jei.34.5.051002","url":null,"abstract":"Color manipulation is a significant topic not only in artistic fields, such as film production and photography, but also in research areas such as medical imaging and forensics, where it has been extensively studied. One prominent approach in this domain focuses on automatically transferring color statistics from one image to another, a process known as color transfer. Over the past few decades, a substantial number of algorithms have been developed and refined to perform color transfer on various data types, including images, videos, point clouds, and light fields. To address the limited accessibility and usability of some existing algorithms, we introduce a new version of ColorTransferLab, a web-based software testbed designed for the straightforward application of a wide range of color transfer, style transfer, and colorization algorithms. This platform extends support to textured 3D meshes, volumetric videos, and Gaussian splattings, alongside the previously mentioned data types. It also provides access to objective metrics widely used for evaluating the quality of color transfer results over recent decades, complementing visual assessments. ColorTransferLabV2 is intended to serve as a valuable tool for researchers and artists in the field of automatic color grading, enabling experimentation with color transfer techniques across multiple media.","PeriodicalId":54843,"journal":{"name":"Journal of Electronic Imaging","volume":"34 05","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147885799","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

Light field salient object detection network based on feature enhancement and mutual attention 基于特征增强和相互注意的光场突出物体检测网络

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-09-01 DOI: 10.1117/1.jei.33.5.053001

Xi Zhu, Huai Xia, Xucheng Wang, Zhenrong Zheng

{"title":"Light field salient object detection network based on feature enhancement and mutual attention","authors":"Xi Zhu, Huai Xia, Xucheng Wang, Zhenrong Zheng","doi":"10.1117/1.jei.33.5.053001","DOIUrl":"https://doi.org/10.1117/1.jei.33.5.053001","url":null,"abstract":"Light field salient object detection (SOD) is an essential research topic in computer vision, but robust saliency detection in complex scenes is still very challenging. We propose a new method for accurate and robust light field SOD via convolutional neural networks containing feature enhancement modules. First, the light field dataset is extended by geometric transformations such as stretching, cropping, flipping, and rotating. Next, two feature enhancement modules are designed to extract features from RGB images and depth maps, respectively. The obtained feature maps are fed into a two-stream network to train the light field SOD. We propose a mutual attention approach in this process, extracting and fusing features from RGB images and depth maps. Therefore, our network can generate an accurate saliency map from the input light field images after training. The obtained saliency map can provide reliable a priori information for tasks such as semantic segmentation, target recognition, and visual tracking. Experimental results show that the proposed method achieves excellent detection performance in public benchmark datasets and outperforms the state-of-the-art methods. We also verify the generalization and stability of the method in real-world experiments.","PeriodicalId":54843,"journal":{"name":"Journal of Electronic Imaging","volume":"8 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202397","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

Small space target detection using megapixel resolution CeleX-V camera 利用百万像素分辨率的 CeleX-V 摄像机探测小空间目标

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-09-01 DOI: 10.1117/1.jei.33.5.053002

Yuanyuan Lv, Liang Zhou, Zhaohui Liu, Wenlong Qiao, Haiyang Zhang

引用次数: 0

Video anomaly detection based on frame memory bank and decoupled asymmetric convolutions 基于帧记忆库和解耦非对称卷积的视频异常检测

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-09-01 DOI: 10.1117/1.jei.33.5.053006

Min Zhao, Chuanxu Wang, Jiajiong Li, Zitai Jiang

{"title":"Video anomaly detection based on frame memory bank and decoupled asymmetric convolutions","authors":"Min Zhao, Chuanxu Wang, Jiajiong Li, Zitai Jiang","doi":"10.1117/1.jei.33.5.053006","DOIUrl":"https://doi.org/10.1117/1.jei.33.5.053006","url":null,"abstract":"Video anomaly detection (VAD) is essential for monitoring systems. The prediction-based methods identify anomalies by comparing differences between the predicted and real frames. We propose an unsupervised VAD method based on frame memory bank (FMB) and decoupled asymmetric convolution (DAConv), which addresses three problems encountered with auto-encoders (AE) in VAD: (1) how to mitigate the noise resulting from jittering between frames, which is ignored; (2) how to alleviate the insufficient utilization of temporal information by traditional two-dimensional (2D) convolution and the burden for more computing resources in three-dimensional (3D) convolution; and (3) how to make full use of normal data to improve the reliability of anomaly discrimination. Specifically, we initially design a separate network to calibrate video frames within the dataset. Second, we design DAConv to extract features from the video, addressing the absence of temporal dimension information in 2D convolutions and the high computational complexity of 3D convolutions. Concurrently, the interval-frame mechanism mitigates the problem of information redundancy caused by data reuse. Finally, we embed an FMB to store features of normal events, amplifying the contrast between normal and abnormal frames. We conduct extensive experiments on the UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets, achieving AUC values of 98.7%, 90.4%, and 74.8%, respectively, which fully demonstrates the rationality and effectiveness of the proposed method.","PeriodicalId":54843,"journal":{"name":"Journal of Electronic Imaging","volume":"105 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202395","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

Attention-injective scale aggregation network for crowd counting 用于人群计数的注意力注入式规模聚合网络

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-09-01 DOI: 10.1117/1.jei.33.5.053008

Haojie Zou, Yingchun Kuang, Jianqiang Luo, Mingwei Yao, Haoyu Zhou, Sha Yang

{"title":"Attention-injective scale aggregation network for crowd counting","authors":"Haojie Zou, Yingchun Kuang, Jianqiang Luo, Mingwei Yao, Haoyu Zhou, Sha Yang","doi":"10.1117/1.jei.33.5.053008","DOIUrl":"https://doi.org/10.1117/1.jei.33.5.053008","url":null,"abstract":"Crowd counting has gained widespread attention in the fields of public safety management, video surveillance, and emergency response. Currently, background interference and scale variation of the head are still intractable problems. We propose an attention-injective scale aggregation network (ASANet) to cope with the above problems. ASANet consists of three parts: shallow feature attention network (SFAN), multi-level feature aggregation (MLFA) module, and density map generation (DMG) network. SFAN effectively overcomes the noise impact of a cluttered background by cross-injecting the attention module in the truncated VGG16 structure. To fully utilize the multi-scale crowd information embedded in the feature layers at different positions, we densely connect the multi-layer feature maps in the MLFA module to solve the scale variation problem. In addition, to capture large-scale head information, the DMG network introduces successive dilated convolutional layers to further expand the receptive field of the model, thus improving the accuracy of crowd counting. We conduct extensive experiments on five public datasets (ShanghaiTech Part_A, ShanghaiTech Part_B, UCF_QNRF, UCF_CC_50, JHU-Crowd++), and the results show that ASANet outperforms most of the existing methods in terms of counting and at the same time demonstrates satisfactory superiority in dealing with background noise in different scenes.","PeriodicalId":54843,"journal":{"name":"Journal of Electronic Imaging","volume":"94 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202400","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

Infrared and visible image fusion based on global context network 基于全球背景网络的红外和可见光图像融合

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-09-01 DOI: 10.1117/1.jei.33.5.053016

Yonghong Li, Yu Shi, Xingcheng Pu, Suqiang Zhang

{"title":"Infrared and visible image fusion based on global context network","authors":"Yonghong Li, Yu Shi, Xingcheng Pu, Suqiang Zhang","doi":"10.1117/1.jei.33.5.053016","DOIUrl":"https://doi.org/10.1117/1.jei.33.5.053016","url":null,"abstract":"Thermal radiation and texture data from two different sensor types are usually combined in the fusion of infrared and visible images for generating a single image. In recent years, convolutional neural network (CNN) based on deep learning has become the mainstream technology for many infrared and visible image fusion methods, which often extracts shallow features and ignores the role of long-range dependencies in the fusion task. However, due to its local perception characteristics, CNN can only obtain global contextual information by continuously stacking convolutional layers, which leads to low network efficiency and difficulty in optimization. To address this issue, we proposed a global context fusion network (GCFN) to model context using a global attention pool, which adopts a two-stage strategy. First, a GCFN-based autoencoder network is trained for extracting multi-scale local and global contextual features. To effectively incorporate the complementary information of the input image, a dual branch fusion network combining CNN and transformer is designed in the second step. Experimental results on a publicly available dataset demonstrate that the proposed method outperforms nine advanced methods in fusion performance on both subjective and objective metrics.","PeriodicalId":54843,"journal":{"name":"Journal of Electronic Imaging","volume":"23 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142258310","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

Generative object separation in X-ray images X 射线图像中的生成物体分离

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-09-01 DOI: 10.1117/1.jei.33.5.053004

Xiaolong Zheng, Yu Zhou, Jia Yao, Liang Zheng

{"title":"Generative object separation in X-ray images","authors":"Xiaolong Zheng, Yu Zhou, Jia Yao, Liang Zheng","doi":"10.1117/1.jei.33.5.053004","DOIUrl":"https://doi.org/10.1117/1.jei.33.5.053004","url":null,"abstract":"X-ray imaging is essential for security inspection; nevertheless, the penetrability of X-rays can cause objects within a package to overlap in X-ray images, leading to reduced accuracy in manual inspection and increased difficulty in auxiliary inspection techniques. Existing methods mainly focus on object detection to enhance the detection ability of models for overlapping regions by augmenting image features, including color, texture, and semantic information. However, these approaches do not address the underlying issue of overlap. We propose a novel method for separating overlapping objects in X-ray images from the perspective of image inpainting. Specifically, the separation method involves using a vision transformer (ViT) to construct a generative adversarial network (GAN) model that requires a hand-created trimap as input. In addition, we present an end-to-end approach that integrates Mask Region-based Convolutional Neural Network with the separation network to achieve fully automated separation of overlapping objects. Given the lack of datasets appropriate for training separation networks, we created MaskXray, a collection of X-ray images that includes overlapping images, trimap, and individual object images. Our proposed generative separation network was tested in experiments and demonstrated its ability to accurately separate overlapping objects in X-ray images. These results demonstrate the efficacy of our approach and make significant contributions to the field of X-ray image analysis.","PeriodicalId":54843,"journal":{"name":"Journal of Electronic Imaging","volume":"4 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202393","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

Vis-YOLO: a lightweight and efficient image detector for unmanned aerial vehicle small objects Vis-YOLO：轻便高效的无人飞行器小型物体图像探测器

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-09-01 DOI: 10.1117/1.jei.33.5.053003

Xiangyu Deng, Jiangyong Du

引用次数: 0

Double-level deep multi-view collaborative learning for image clustering 图像聚类的双层深度多视角协作学习

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-09-01 DOI: 10.1117/1.jei.33.5.053012

Liang Xiao, Wenzhe Liu

{"title":"Double-level deep multi-view collaborative learning for image clustering","authors":"Liang Xiao, Wenzhe Liu","doi":"10.1117/1.jei.33.5.053012","DOIUrl":"https://doi.org/10.1117/1.jei.33.5.053012","url":null,"abstract":"Multi-view clustering has garnered significant attention due to its ability to explore shared information from multiple views. Applications of multi-view clustering include image and video analysis, bioinformatics, and social network analysis, in which integrating diverse data sources enhances data understanding and insights. However, existing multi-view models suffer from the following limitations: (1) directly extracting latent representations from raw data using encoders is susceptible to interference from noise and other factors and (2) complementary information among different views is often overlooked, resulting in the loss of crucial unique information from each view. Therefore, we propose a distinctive double-level deep multi-view collaborative learning approach. Our method further processes the latent representations learned by the encoder through multiple layers of perceptrons to obtain richer semantic information. In addition, we introduce dual-path guidance at both the feature and label levels to facilitate the learning of complementary information across different views. Furthermore, we introduce pre-clustering methods to guide mutual learning among different views through pseudo-labels. Experimental results on four image datasets (Caltech-5V, STL10, Cifar10, Cifar100) demonstrate that our method achieves state-of-the-art clustering performance, evaluated using standard metrics, including accuracy, normalized mutual information, and purity. We compare our proposed method with existing clustering algorithms to validate its effectiveness.","PeriodicalId":54843,"journal":{"name":"Journal of Electronic Imaging","volume":"6 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202402","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