2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)最新文献_第9页

Lightweight Color Image Demosaicking with Multi-Core Feature Extraction 轻量级彩色图像去马赛克与多核特征提取

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301841

Yufei Tan, Kan Chang, Hengxin Li, Zhenhua Tang, Tuanfa Qin

{"title":"Lightweight Color Image Demosaicking with Multi-Core Feature Extraction","authors":"Yufei Tan, Kan Chang, Hengxin Li, Zhenhua Tang, Tuanfa Qin","doi":"10.1109/VCIP49819.2020.9301841","DOIUrl":"https://doi.org/10.1109/VCIP49819.2020.9301841","url":null,"abstract":"Convolutional neural network (CNN)-based color image demosaicking methods have achieved great success recently. However, in many applications where the computation resource is highly limited, it is not practical to deploy large-scale networks. This paper proposes a lightweight CNN for color image demosaicking. Firstly, to effectively extract shallow features, a multi-core feature extraction module, which takes the Bayer sampling positions into consideration, is proposed. Secondly, by taking advantage of inter-channel correlation, an attention-aware fusion module is presented to efficiently r econstruct t he full color image. Moreover, a feature enhancement module, which contains several cascading attention-aware enhancement blocks, is designed to further refine t he i nitial reconstructed i mage. To demonstrate the effectiveness of the proposed network, several state-of-the-art demosaicking methods are compared. Experimental results show that with the smallest number of parameters, the proposed network outperforms the other compared methods in terms of both objective and subjective qualities.","PeriodicalId":431880,"journal":{"name":"2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123443993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Unified Single Image De-raining Model via Region Adaptive Coupled Network 基于区域自适应耦合网络的统一单幅图像去训练模型

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301865

Q. Wu, Li Chen, K. Ngan, Hongliang Li, Fanman Meng, Linfeng Xu

{"title":"A Unified Single Image De-raining Model via Region Adaptive Coupled Network","authors":"Q. Wu, Li Chen, K. Ngan, Hongliang Li, Fanman Meng, Linfeng Xu","doi":"10.1109/VCIP49819.2020.9301865","DOIUrl":"https://doi.org/10.1109/VCIP49819.2020.9301865","url":null,"abstract":"Single image de-raining is quite challenging due to the diversity of rain types and inhomogeneous distributions of rainwater. By means of dedicated models and constraints, existing methods perform well for specific rain type. However, their generalization capability is highly limited as well. In this paper, we propose a unified de-raining model by selectively fusing the clean background of the input rain image and the well restored regions occluded by various rains. This is achieved by our region adaptive coupled network (RACN), whose two branches integrate the features of each other in different layers to jointly generate the spatial-variant weight and restored image respectively. On the one hand, the weight branch could lead the restoration branch to focus on the regions with higher contributions for de-raining. On the other hand, the restoration branch could guide the weight branch to keep off the regions with over-/under-filtering risks. Extensive experiments show that our method outperforms many state-of-the-art de-raining algorithms on diverse rain types including the rain streak, raindrop and rain-mist.","PeriodicalId":431880,"journal":{"name":"2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123490851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Spatiotemporal Guided Self-Supervised Depth Completion from LiDAR and Monocular Camera 基于激光雷达和单目相机的时空制导自监督深度完成

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301857

Z. Chen, Hantao Wang, Lijun Wu, Yanlin Zhou, Dapeng Oliver Wu

引用次数: 5

DEN: Disentanglement and Enhancement Networks for Low Illumination Images DEN:低照度图像的解纠缠和增强网络

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301830

Nelson Chong Ngee Bow, Vu-Hoang Tran, Punchok Kerdsiri, Y. P. Loh, Ching-Chun Huang

{"title":"DEN: Disentanglement and Enhancement Networks for Low Illumination Images","authors":"Nelson Chong Ngee Bow, Vu-Hoang Tran, Punchok Kerdsiri, Y. P. Loh, Ching-Chun Huang","doi":"10.1109/VCIP49819.2020.9301830","DOIUrl":"https://doi.org/10.1109/VCIP49819.2020.9301830","url":null,"abstract":"Though learning-based low-light enhancement methods have achieved significant success, existing methods are still sensitive to noise and unnatural appearance. The problems may come from the lack of structural awareness and the confusion between noise and texture. Thus, we present a lowlight image enhancement method that consists of an image disentanglement network and an illumination boosting network. The disentanglement network is first used to decompose the input image into image details and image illumination. The extracted illumination part then goes through a multi-branch enhancement network designed to improve the dynamic range of the image. The multi-branch network extracts multi-level image features and enhances them via numerous subnets. These enhanced features are then fused to generate the enhanced illumination part. Finally, the denoised image details and the enhanced illumination are entangled to produce the normallight image. Experimental results show that our method can produce visually pleasing images in many public datasets.","PeriodicalId":431880,"journal":{"name":"2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126428146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HDR Image Compression with Convolutional Autoencoder 卷积自编码器的HDR图像压缩

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301853

Fei Han, Jin Wang, Ruiqin Xiong, Qing Zhu, Baocai Yin

{"title":"HDR Image Compression with Convolutional Autoencoder","authors":"Fei Han, Jin Wang, Ruiqin Xiong, Qing Zhu, Baocai Yin","doi":"10.1109/VCIP49819.2020.9301853","DOIUrl":"https://doi.org/10.1109/VCIP49819.2020.9301853","url":null,"abstract":"As one of the next-generation multimedia technology, high dynamic range (HDR) imaging technology has been widely applied. Due to its wider color range, HDR image brings greater compression and storage burden compared with traditional LDR image. To solve this problem, in this paper, a two-layer HDR image compression framework based on convolutional neural networks is proposed. The framework is composed of a base layer which provides backward compatibility with the standard JPEG, and an extension layer based on a convolutional variational autoencoder neural networks and a post-processing module. The autoencoder mainly includes a nonlinear transform encoder, a binarized quantizer and a nonlinear transform decoder. Compared with traditional codecs, the proposed CNN autoencoder is more flexible and can retain more image semantic information, which will improve the quality of decoded HDR image. Moreover, to reduce the compression artifacts and noise of reconstructed HDR image, a post-processing method based on group convolutional neural networks is designed. Experimental results show that our method outperforms JPEG XT profile A, B, C and other methods in terms of HDR-VDP-2 evaluation metric. Meanwhile, our scheme also provides backward compatibility with the standard JPEG.","PeriodicalId":431880,"journal":{"name":"2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116808239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

GRNet: Deep Convolutional Neural Networks based on Graph Reasoning for Semantic Segmentation GRNet:基于图推理的深度卷积神经网络语义分割

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301851

Yang Wu, A. Jiang, Yibin Tang, H. Kwan

引用次数: 2

Random-access-aware Light Field Video Coding using Tree Pruning Method 基于树修剪方法的随机访问感知光场视频编码

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301800

T. N. Huu, V. V. Duong, B. Jeon

{"title":"Random-access-aware Light Field Video Coding using Tree Pruning Method","authors":"T. N. Huu, V. V. Duong, B. Jeon","doi":"10.1109/VCIP49819.2020.9301800","DOIUrl":"https://doi.org/10.1109/VCIP49819.2020.9301800","url":null,"abstract":"The increasing prevalence of VR/AR as well as the expected availability of Light Field (LF) display soon call for more practical methods to transmit LF image/video for services. In that aspect, the LF video coding should not only consider the compression efficiency but also the view random-access capability (especially in the multi-view-based system). The multi-view coding system heavily exploits view dependencies coming from both inter-view and temporal correlation. While such a system greatly improves the compression efficiency, its view random-access capability can be much reduced due to so called \"chain of dependencies.\" In this paper, we first model the chain of dependencies by a tree, then a cost function is used to assign an importance value to each tree node. By travelling from top to bottom, a node of lesser importance is cut-off, forming a pruned tree to achieve reduction of random-access complexity. Our tree pruning method has shown to reduce about 40% of random-access complexity at the cost of minor compression loss compared to the state-of-the-art methods. Furthermore, it is expected that our method is very lightweight in its realization and also effective on a practical LF video coding system.","PeriodicalId":431880,"journal":{"name":"2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117140852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

On 2D-3D Image Feature Detections for Image-To-Geometry Registration in Virtual Dental Model 虚拟牙齿模型图像-几何配准中2D-3D图像特征检测研究

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301774

Hui-jun Tang, R. T. Hsung, W. Y. Lam, Leo Y. Y. Cheng, E. Pow

{"title":"On 2D-3D Image Feature Detections for Image-To-Geometry Registration in Virtual Dental Model","authors":"Hui-jun Tang, R. T. Hsung, W. Y. Lam, Leo Y. Y. Cheng, E. Pow","doi":"10.1109/VCIP49819.2020.9301774","DOIUrl":"https://doi.org/10.1109/VCIP49819.2020.9301774","url":null,"abstract":"3D digital smile design (DSD) gains great interest in dentistry because it enables esthetic design of teeth and gum. However, the color texture of teeth and gum is often lost/distorted in the digitization process. Recently, the image-to-geometry registration shade mapping (IGRSM) method was proposed for registering color texture from 2D photography to 3D mesh model. It allows better control of illumination and color calibration for automatic teeth shade matching. In this paper, we investigate automated techniques to find the correspondences between 3D tooth model and color intraoral photographs for accurately perform the IGRSM. We propose to use the tooth cusp tips as the correspondence points for the IGR because they can be reliably detected both in 2D photography and 3D surface scan. A modified gradient descent method with directional priority (GDDP) and region growing are developed to find the 3D correspondence points. For the 2D image, the tooth tips contour lines are extracted based on luminosity and chromaticity, the contour peaks are then detected as the correspondence points. From the experimental results, the proposed method shows excellent accuracy in detecting the correspondence points between 2D photography and 3D tooth model. The average registration error is less than 15 pixels for 4752×3168 size intraoral image.","PeriodicalId":431880,"journal":{"name":"2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121720311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A semantic labeling framework for ALS point clouds based on discretization and CNN 基于离散化和CNN的ALS点云语义标注框架

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301759

Xingtao Wang, Xiaopeng Fan, Debin Zhao

引用次数: 0

Orthogonal Features Fusion Network for Anomaly Detection 基于正交特征融合网络的异常检测

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301755

Teli Ma, Yizhi Wang, Jinxin Shao, Baochang Zhang, D. Doermann

{"title":"Orthogonal Features Fusion Network for Anomaly Detection","authors":"Teli Ma, Yizhi Wang, Jinxin Shao, Baochang Zhang, D. Doermann","doi":"10.1109/VCIP49819.2020.9301755","DOIUrl":"https://doi.org/10.1109/VCIP49819.2020.9301755","url":null,"abstract":"Generative models have been successfully used for anomaly detection, which however need a large number of parameters and computation overheads, especially when training spatial and temporal networks in the same framework. In this paper, we introduce a novel network architecture, Orthogonal Features Fusion Network (OFF-Net), to solve the anomaly detection problem. We show that the convolutional feature maps used for generating future frames are orthogonal with each other, which can improve representation capacity of generative models and strengthen temporal connections between adjacent images. We lead a simple but effective module easily mounted on convolutional neural networks (CNNs) with negligible additional parameters added, which can replace the widely-used optical flow n etwork a nd s ignificantly im prove th e pe rformance for anomaly detection. Extensive experiment results demonstrate the effectiveness of OFF-Net that we outperform the state-of-the-art model 1.7% in terms of AUC. We save around 85M-space parameters compared with the prevailing prior arts using optical flow n etwork w ithout c omprising t he performance.","PeriodicalId":431880,"journal":{"name":"2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129733259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0