Journal of Electronic Imaging最新文献_第7页

Scale separation: video crowd counting with different density maps 规模分离：使用不同密度图进行视频人群计数

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-07-01 DOI: 10.1117/1.jei.33.4.043016

Ao Zhang, Xin Deng, Baoying Liu, Weiwei Zhang, Jun Guo, Linrui Xie

引用次数: 0

Early quadtree with nested multitype tree partitioning algorithm based on convolution neural network for the versatile video coding standard 基于卷积神经网络的早期四叉树嵌套多类型树分区算法，适用于多功能视频编码标准

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-07-01 DOI: 10.1117/1.jei.33.4.043024

Bouthaina Abdallah, Sonda Ben Jdidia, Fatma Belghith, Mohamed Ali Ben Ayed, Nouri Masmoudi

{"title":"Early quadtree with nested multitype tree partitioning algorithm based on convolution neural network for the versatile video coding standard","authors":"Bouthaina Abdallah, Sonda Ben Jdidia, Fatma Belghith, Mohamed Ali Ben Ayed, Nouri Masmoudi","doi":"10.1117/1.jei.33.4.043024","DOIUrl":"https://doi.org/10.1117/1.jei.33.4.043024","url":null,"abstract":"The Joint Video Experts Team has recently finalized the versatile video coding (VVC) standard, which incorporates various advanced encoding tools. These tools ensure great enhancements in the coding efficiency, leading to a bitrate reduction up to 50% when compared to the previous standard, high-efficiency video coding. However, this enhancement comes at the expense of high computational complexity. Within this context, we address the new quadtree (QT) with nested multitype tree partition block in VVC for all-intra configuration. In fact, we propose a fast intra-coding unit (CU) partition algorithm using various convolution neural network (CNN) classifiers to directly predict the partition mode, skip unnecessary split modes, and early exit the partitioning process. The proposed approach first predicts the QT depth at a CU of size 64×64 by the corresponding CNN classifier. Then four CNN classifiers are applied to predict the partition decision tree at a CU of size 32×32 using multithreshold values and ignore the rate-distortion optimization process to speed up the partition coding time. Thus the developed method is implemented on the reference software VTM 16.2 and tested for different video sequences. The experimental results confirm that the proposed solution achieves an encoding time reduction of about 46% in average, reaching up to 67.3% with an acceptable increase in bitrate and an unsignificant decrease in quality.","PeriodicalId":54843,"journal":{"name":"Journal of Electronic Imaging","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745421","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

Background-focused contrastive learning for unpaired image-to-image translation 针对无配对图像到图像翻译的背景聚焦对比学习

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-07-01 DOI: 10.1117/1.jei.33.4.043023

Mingwen Shao, Minggui Han, Lingzhuang Meng, Fukang Liu

{"title":"Background-focused contrastive learning for unpaired image-to-image translation","authors":"Mingwen Shao, Minggui Han, Lingzhuang Meng, Fukang Liu","doi":"10.1117/1.jei.33.4.043023","DOIUrl":"https://doi.org/10.1117/1.jei.33.4.043023","url":null,"abstract":"Contrastive learning for unpaired image-to-image translation (CUT) aims to learn a mapping from source to target domain with an unpaired dataset, which combines contrastive loss to maximize the mutual information between real and generated images. However, the existing CUT-based methods exhibit unsatisfactory visual quality due to the wrong locating of objects and backgrounds, particularly where it incorrectly transforms the background to match the object pattern in layout-changing datasets. To alleviate the issue, we present background-focused contrastive learning for unpaired image-to-image translation (BFCUT) to improve the background’s consistency between real and its generated images. Specifically, we first generate heat maps to explicitly locate the objects and backgrounds for subsequent contrastive loss and global background similarity loss. Then, the representative queries of objects and backgrounds rather than randomly sampling queries are selected for contrastive loss to promote reality of objects and maintenance of backgrounds. Meanwhile, global semantic vectors with less object information are extracted with the help of heat maps, and we further align the vectors of real images and their corresponding generated images to promote the maintenance of the backgrounds in global background similarity loss. Our BFCUT alleviates the wrong translation of backgrounds and generates more realistic images. Extensive experiments on three datasets demonstrate better quantitative results and qualitative visual effects.","PeriodicalId":54843,"journal":{"name":"Journal of Electronic Imaging","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745419","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

High-resolution cloud detection network 高分辨率云检测网络

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-07-01 DOI: 10.1117/1.jei.33.4.043027

Jingsheng Li, Tianxiang Xue, Jiayi Zhao, Jingmin Ge, Yufang Min, Wei Su, Kun Zhan

引用次数: 0

Event-frame object detection under dynamic background condition 动态背景条件下的事件帧物体检测

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-07-01 DOI: 10.1117/1.jei.33.4.043028

Wenhao Lu, Zehao Li, Junying Li, Yuncheng Lu, Tony Tae-Hyoung Kim

{"title":"Event-frame object detection under dynamic background condition","authors":"Wenhao Lu, Zehao Li, Junying Li, Yuncheng Lu, Tony Tae-Hyoung Kim","doi":"10.1117/1.jei.33.4.043028","DOIUrl":"https://doi.org/10.1117/1.jei.33.4.043028","url":null,"abstract":"Neuromorphic vision sensors (NVS) with the features of small data redundancy and transmission latency are widely implemented in Internet of Things applications. Previous studies have developed various object detection algorithms based on NVS’s unique event data format. However, most of these methods are only adaptive for scenarios with stationary backgrounds. Under dynamic background conditions, NVS can also acquire the events of non-target objects due to its mechanism of detecting pixel intensity changes. As a result, the performance of existing detection methods is greatly degraded. To address this shortcoming, we introduce an extra refinement process to the conventional histogram-based (HIST) detection method. For the proposed regions from HIST, we apply a practical decision condition to categorize them as either object-dominant or background-dominant cases. Then, the object-dominant regions undergo a second-time HIST-based region proposal for precise localization, while background-dominant regions employ an upper outline determination strategy for target object identification. Finally, the refined results are tracked using a simplified Kalman filter approach. Evaluated in an outdoor drone surveillance with an event camera, the proposed scheme demonstrates superior performance in both intersection over union and F1 score metrics compared to other methods.","PeriodicalId":54843,"journal":{"name":"Journal of Electronic Imaging","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771012","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

Robust video hashing with canonical polyadic decomposition and Hahn moments 使用正则多面体分解和哈恩矩的鲁棒视频散列技术

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-07-01 DOI: 10.1117/1.jei.33.4.043007

Zhenjun Tang, Huijiang Zhuang, Mengzhu Yu, Lv Chen, Xiaoping Liang, Xianquan Zhang

引用次数: 0

Scene adaptive color compensation and multi-weight fusion of underwater image 水下图像的场景自适应色彩补偿和多权重融合

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-07-01 DOI: 10.1117/1.jei.33.4.043031

Muhammad Aon, Huibing Wang, Muhammad Noman Waleed, Yulin Wei, Xianping Fu

{"title":"Scene adaptive color compensation and multi-weight fusion of underwater image","authors":"Muhammad Aon, Huibing Wang, Muhammad Noman Waleed, Yulin Wei, Xianping Fu","doi":"10.1117/1.jei.33.4.043031","DOIUrl":"https://doi.org/10.1117/1.jei.33.4.043031","url":null,"abstract":"Capturing high-quality photos in an underwater atmosphere is complicated, as light attenuation, color distortion, and reduced contrast pose significant challenges. However, one fact usually ignored is the non-uniform texture degradation in distorted images. The loss of comprehensive textures in underwater images poses obstacles in object detection and recognition. To address this problem, we have introduced an image enhancement model called scene adaptive color compensation and multi-weight fusion for extracting fine textural details under diverse environments and enhancing the overall quality of the underwater imagery. Our method blends three input images derived from the adaptive color-compensating and color-corrected version of the degraded image. The first two input images are used to adjust the low contrast and dehazing of the image respectively. Similarly, the third input image is used to extract the fine texture details based on different scales and orientations of the image. Finally, the input images with their associated weight maps are normalized and fused through multi-weight fusion. The proposed model is tested on a distinct set of underwater imagery with varying levels of degradation and frequently outperformed state-of-the-art methods, producing significant improvements in texture visibility, reducing color distortion, and enhancing the overall quality of the submerged images.","PeriodicalId":54843,"journal":{"name":"Journal of Electronic Imaging","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771010","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

Hyperspectral image denoising via self-modulated cross-attention deformable convolutional neural network 通过自调制交叉注意可变形卷积神经网络实现高光谱图像去噪

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-07-01 DOI: 10.1117/1.jei.33.4.043015

Ying Wang, Jie Qiu, Yanxiang Zhao

{"title":"Hyperspectral image denoising via self-modulated cross-attention deformable convolutional neural network","authors":"Ying Wang, Jie Qiu, Yanxiang Zhao","doi":"10.1117/1.jei.33.4.043015","DOIUrl":"https://doi.org/10.1117/1.jei.33.4.043015","url":null,"abstract":"Compared with ordinary images, hyperspectral images (HSIs) consist of many bands that can provide rich spatial and spectral information and are widely used in remote sensing. However, HSIs are subject to various types of noise due to limited sensor sensitivity; low light intensity in the bands; and corruption during acquisition, transmission, and storage. Therefore, the problem of HSI denoising has attracted extensive attention from society. Although recent HSI denoising methods provide effective solutions in various optimization directions, their performance under real complex noise is still not optimal. To address these issues, this article proposes a self-modulated cross-attention network that fully utilizes spatial and spectral information. The core of the model is the use of deformable convolution to cross-fuse spatial and spectral features to improve the network denoising capability. At the same time, a self-modulating residual block allows the network to transform features in an adaptive manner based on neighboring bands, improving the network’s ability to deal with complex noise, which we call a feature enhancement block. Finally, we propose a three-segment network architecture that improves the stability of the model. The method proposed in this work outperforms other state-of-the-art methods through comparative analysis of experiments in synthetic and real data.","PeriodicalId":54843,"journal":{"name":"Journal of Electronic Imaging","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141612603","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

Symmetric image compression network with improved normalization attention mechanism 具有改进的归一化关注机制的对称图像压缩网络

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-06-11 DOI: 10.1117/1.jei.33.3.033028

Shen-Chuan Tai, Chia-Mao Yeh, Yu-Ting Lee, Wesley Huang

引用次数: 0

Spatio-temporal co-attention fusion network for video splicing localization 用于视频拼接定位的时空共关注融合网络

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-06-07 DOI: 10.1117/1.jei.33.3.033027

Man Lin, Gang Cao, Zijie Lou, Chi Zhang

引用次数: 0