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DEN: Disentanglement and Enhancement Networks for Low Illumination images

DEN: Disentanglement and Enhancement Networks for Low Illumi...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Bow, Nelson Chong Ngee Vu-Hoang Tran Kerdsiri, Punchok Loh, Yuen Peng Huang, Ching-Chun Multimedia Univ Fac Comp & Informat Cyberjaya Selangor Malaysia Ho Chi Minh City Univ Technol & Educ Fac Elect & Elect Engn Ho Chi Minh City Vietnam PTT Explorat & Prod PCL DCOE Digital Ctr Excellence Bangkok Thailand Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9781728180687

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 low-light 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 normal-light image. Experimental results show that our method can produce visually pleasing images in many public datasets

关键词： low-light enhancement image disentanglement multi-branch enhancement network

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No-Reference Stereoscopic image Quality Assessment Considering Multi-loss Constraints

No-Reference Stereoscopic Image Quality Assessment Consideri...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Han, Yongtian Li, Sumei Yue, Guanghui Chang, Yongli Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China Shenzhen Univ Sch Biomed Engn Hlth Sci Ctr Shenzhen Peoples R China

ISBN: (纸本)9781728180687

In this paper, a three-channel convolutional neural network (CNN) constrained by multiple loss functions is designed for stereoscopic image quality assessment (SIQA). Given that both monocular and binocular information are crucial for SIQA, we take the patches of left images, right images and difference images as the inputs of the three channels respectively. Since using the ground truth as the labels of image patches cannot accurately characterize their quality, we propose to individually label each image patch to preserve the quality difference among different regions and views. Moreover, the multi-loss structure is adopted in the proposed method to consider both local features and global features simultaneously, which can constrain the feature learning from multiple perspectives. And the additional adaptive loss weights make the multi-loss network more flexible and universal. The experimental results show that the proposed method is superior to other existing SIQA methods with state-of-the-art performance.

关键词： stereoscopic image quality assessment multi-loss proxy labels adaptive loss weights convolutional neural network

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Spatial-Channel Context-Based Entropy Modeling for End-to-end Optimized image Compression

Spatial-Channel Context-Based Entropy Modeling for End-to-en...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Li, Chongxin Luo, Jixiang Dai, Wenrui Li, Chenglin Zou, Junni Xiong, Hongkai Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781728180687

End-to-end optimized image compression has emerged as a disruptive technique to reduce the spatial redundancies with an improved reconstruction quality. However, existing entropy model for latent representations cannot sufficiently exploit their spatial and channel-wise correlations. In this paper, we propose a novel entropy model based on spatial-channel contexts for end-to-end optimized image compression. The proposed model jointly leverages spatial structural dependencies and channel-wise correlations to improve the probabilistic estimation of latent representations. Instead of complex autoregressive hyperprior network, shallow artificial neural networks (ANNs) incorporating 3-D masks are developed to efficiently realize the entropy model with a guarantee of causality. Experimental results demonstrate that the proposed model achieves competitive rate-distortion performance and reduces model complexity in comparison to recent end-to-end optimized methods for image compression.

关键词： End-to-end optimized image compression context-based entropy coding artificial neural networks

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News image Steganography: A Novel Architecture Facilitates the Fake News Identification

News Image Steganography: A Novel Architecture Facilitates t...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Zhou, Jizhe Pun, Chi-Man Tong, Yu Univ Macao Dept Comp & Informat Sci Macau Peoples R China Vivo Mobile Commun Co Ltd Vivo AI Lab Shenzhen Peoples R China

ISBN: (纸本)9781728180687

A larger portion of fake news quotes untampered images from other sources with ulterior motives rather than conducting image forgery. Such elaborate engraftments keep the inconsistency between images and text reports stealthy, thereby, palm off the spurious for the genuine. This paper proposes an architecture named News image Steganography (NIS) to reveal the aforementioned inconsistency through image steganography based on GAN. Extractive summarization about a news image is generated based on its source texts, and a learned steganographic algorithm encodes and decodes the summarization of the image in a manner that approaches perceptual invisibility. Once an encoded image is quoted, its source summarization can be decoded and further presented as the ground truth to verify the quoting news. The pairwise encoder and decoder endow images of the capability to carry along their imperceptible summarization. Our NIS reveals the underlying inconsistency, thereby, according to our experiments and investigations, contributes to the identification accuracy of fake news that engrafts untampered images.

关键词： image steganography information hiding fake news identification

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Hierarchical Reinforcement Learning Based Video Semantic Coding for Segmentation

Hierarchical Reinforcement Learning Based Video Semantic Cod...

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IEEE visual communications and image processing (VCIP)

作者： Guangqi Xie Xin Li Shiqi Lin Zhibo Chen Li Zhang Kai Zhang Yue Li Unviersity of Science and Technology of China Hefei China ByteDance Inc.

ISBN: (纸本)9781665475938

The rapid development of intelligent tasks, e.g., segmentation, detection, and classification, etc, has brought an urgent need for semantic compression, which aims to reduce the compression cost while maintaining the original semantic information. However, it is impractical to directly integrate the semantic metric into the traditional codecs since they cannot be optimized in an end-to-end manner. To solve this problem, some pioneering works have applied reinforcement learning to implement image-wise semantic compression. Nevertheless, the video semantic compression has not been explored since its complex reference architectures and compression modes. In this paper, we take a step forward to video semantic compression and propose the Hierarchical Reinforcement Learning based task-driven Video Semantic Coding, named as HRLVSC. Specifically, to simplify the complex mode decision of video semantic coding, we divided the action space into frame-level and CTU-level spaces in a hierarchical manner, and then explore the best mode selection for them progressively with the cooperation of frame-level and CTU-level agents. Moreover, since the modes of video semantic coding will exponentially increase with the number of frames in a Group of Pictures (GOP), we carefully investigate the effects of different mode selections for video semantic coding, and design a simple but effective mode simplification strategy for it. We have validated our HRLVSC on video segmentation task with HEVC reference software HM16.19. Extensive experimental results demonstrated that our HRLVSC can achieve over 39% BD-rate saving for video semantic coding under the Low Delay P configuration.

关键词： Video coding Training image segmentation image coding visual communication Semantics Reinforcement learning

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3D-CNN Autoencoder for Plenoptic image Compression

3D-CNN Autoencoder for Plenoptic Image Compression

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IEEE International conference on visual communications and image processing (VCIP)

作者： Zhong, Tingting Jin, Xin Tong, Kedeng Tsinghua Shenzhen Int Grad Sch Shenzhen Key Lab Broadband Network & Multimedia Shenzhen 518055 Peoples R China

ISBN: (纸本)9781728180687

Recently, plenoptic image has attracted great attentions because of its applications in various scenarios. However, high resolution and special pixel distribution structure bring huge challenges to its storage and transmission. In order to adapt compression to the structural characteristic of plenoptic image, in this paper, we propose a Data Structure Adaptive 3D-convolutional(DSA-3D) autoencoder. The DSA-3D autoencoder enables up-sampling and down-samping the sub-aperture sequence along the angular resolution or spatial resolution, thereby avoiding the artifacts caused by directly compressing plenoptic image and achieving better compression efficiency. In addition, we propose a special and efficient S quare rearrangement to generate sub-aperture sequence. We compare Square with Zigzag sub-aperture sequence rearrangements, and analyzed the compression efficiency of block image compression and whole image compression. Compared with traditional hybrid encoders HEVC, JPEG2000 and JPEG PLENO(WaSP), the proposed DSA-3D(Square) autoencoder achieves a superior performance in terms of PSNR metrics.

关键词： Plenoptic image compression Autoencoder network 3D-CNN

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A MULTI-MODEL FUSION FRAMEWORK FOR NIR-TO-RGB TRANSLATION

A MULTI-MODEL FUSION FRAMEWORK FOR NIR-TO-RGB TRANSLATION

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IEEE International conference on visual communications and image processing (VCIP)

作者： Yan, Longbin Wang, Xiuheng Zhao, Min Liu, Shumin Chen, Jie Northwestern Polytech Univ Sch Marine Sci & Technol CIAIC Xian Peoples R China Shanghai Shengyao Intelligent Technol Co Ltd Shanghai Peoples R China Blueye Intelligence Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9781728180687

Near-infrared (NIR) images provide spectral information beyond the visible light spectrum and thus are useful in many applications. However, single-channel NIR images contain less information per pixel than RGB images and lack visibility for human perception. Transforming NIR images to RGB images is necessary for performing further analysis and computer vision tasks. In this work, we propose a novel NIR-to-RGB translation method. It contains two sub-networks and a fusion operator. Specifically, a U-net based neural network is used to learn the texture information while a CycleGAN based neural network is adopted to excavate the color information. Finally, a guided filter based fusion strategy is applied to fuse the outputs of these two neural networks. Experiment results show that our proposed method achieves superior NIR-to-RGB translation performance.

关键词： NIR-to-RGB translation U-net CycleGAN guided filter image fusion

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Human pose based video compression via forward-referencing using deep learning

Human pose based video compression via forward-referencing u...

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IEEE visual communications and image processing (VCIP)

作者： S M Ataul Karim Rajin Manzur Murshed Manoranjan Paul Shyh Wei Teng Jiangang Ma Federation University Australia Deakin University Australia Charles Sturt University Australia

ISBN: (纸本)9781665475938

To exploit high temporal correlations in video frames of the same scene, the current frame is predicted from the already-encoded reference frames using block-based motion estimation and compensation techniques. While this approach can efficiently exploit the translation motion of the moving objects, it is susceptible to other types of affine motion and object occlusion/deocclusion. Recently, deep learning has been used to model the high-level structure of human pose in specific actions from short videos and then generate virtual frames in future time by predicting the pose using a generative adversarial network (GAN). Therefore, modelling the high-level structure of human pose is able to exploit semantic correlation by predicting human actions and determining its trajectory. Video surveillance applications will benefit as stored “big” surveillance data can be compressed by estimating human pose trajectories and generating future frames through semantic correlation. This paper explores a new way of video coding by modelling human pose from the already-encoded frames and using the generated frame at the current time as an additional forward-referencing frame. It is expected that the proposed approach can overcome the limitations of the traditional backward-referencing frames by predicting the blocks containing the moving objects with lower residuals. Our experimental results show that the proposed approach can achieve on average up to 2.83 dB PSNR gain and 25.93% bitrate savings for high motion video sequences compared to standard video coding.

关键词： Video coding Deep learning Correlation image coding visual communication Semantics Predictive models

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An Integrated Model for Generating 3D Point Clouds and Segmentation from 2D images

An Integrated Model for Generating 3D Point Clouds and Segme...

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International conference on Imaging, Signal processing and Communication (ICISPC)

作者： Huan-Yu Chen Chuen-Horng Lin Yan-Yu Lin Yung-Kuan Chan Department of Computer Science and Information Engineering National Taichung University of Science and Technology No. 129 Sec.3 Sanmin Rd Taichung Taiwan R.O.C Department of Management Information Systems National Chung Hsing University 145 Xingda Rd.South Dist Taichung Taiwan R.O.C

ISBN: (数字)9798350367157

ISBN: (纸本)9798350367164

This study presents a cost-effective approach to constructing simulation systems by integrating 2D visual processing with neural network technology for simultaneous 3D point cloud generation and part segmentation, thereby reducing time and costs. We propose multiple task-specific models using 2D RGB images, incorporating attention mechanisms, feature encoding, decoding layers, and innovative loss functions. The optimal models are merged to form an integrated system, evaluated using public and self-built datasets. Our experiments demonstrate that the integrated model effectively reduces training time and Floating Point Operations (FLOP) values while maintaining high accuracy in Critical Hole Distance (CHD), Earth Mover’s Distance (EMD), and Intersection over Union (IOU) metrics. This advancement enhances applications in military training, aerospace technology, and disaster response simulation, facilitating faster 3D object construction.

关键词： Training Point cloud compression image segmentation Solid modeling Adaptation models visualization Three-dimensional displays

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Lightweight Color image Demosaicking with Multi-Core Feature Extraction

Lightweight Color Image Demosaicking with Multi-Core Feature...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Tan, Yufei Chang, Kan Li, Hengxin Tang, Zhenhua Qin, Tuanfa Guangxi Univ Sch Comp & Elect Informat Nanning Peoples R China Guangxi Univ Guangxi Key Lab Multimedia Commun & Network Techn Nanning Peoples R China

ISBN: (纸本)9781728180687

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 r econstructed 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.

关键词： convolutional neural network image demosaicking feature extraction channel attention residual learning

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