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A Unified Single image De-raining Model via Region Adaptive Coupled Network

A Unified Single Image De-raining Model via Region Adaptive ...

引用

ieee international conference on visual communications and image processing (vcip)

作者： Wu, Qingbo Chen, Li Ngan, King Ngi Li, Hongliang Meng, Fanman Xu, Linfeng Univ Elect Sci & Technol China Chengdu 611731 Peoples R China

ISBN: (纸本)9781728180687

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.

关键词： Single image de-raining region adaptive coupled network weighted fusion model

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Lightweight Color image Demosaicking with Multi-Core Feature Extraction

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

引用

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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A review of data preprocessing modules in digital image forensics methods using deep learning

A review of data preprocessing modules in digital image fore...

引用

ieee international conference on visual communications and image processing (vcip)

作者： Berthet, Alexandre Dugelay, Jean-Luc Eurecom Dept Digital Secur Biot France

ISBN: (纸本)9781728180687

Access to technologies like mobile phones contributes to the significant increase in the volume of digital visual data (images and videos). In addition, photo editing software is becoming increasingly powerful and easy to use. In some cases, these tools can be utilized to produce forgeries with the objective to change the semantic meaning of a photo or a video (e.g. fake news). Digital image forensics (DIF) includes two main objectives: the detection (and localization) of forgery and the identification of the origin of the acquisition (i.e. sensor identification). Since 2005, many classical methods for DIF have been designed, implemented and tested on several databases. Meantime, innovative approaches based on deep learning have emerged in other fields a nd have surpassed traditional techniques. In the context of DIF, deep learning methods mainly use convolutional neural networks (CNN) associated with significant preprocessing modules. This is an active domain and two possible ways to operate preprocessing have been studied: prior to the network or incorporated into it. None of the various studies on the digital image forensics provide a comprehensive overview of the preprocessing techniques used with deep learning methods. Therefore, the core objective of this article is to review the preprocessing modules associated with CNN models.

关键词： Semantics

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Special Cane with visual Odometry for Real-time Indoor Navigation of Blind People

Special Cane with Visual Odometry for Real-time Indoor Navig...

引用

ieee international conference on visual communications and image processing (vcip)

作者： Tang, Jingyu Hu, Menghan Li, Guodong Li, Qingli Zhang, Jian Zhou, Xiaofeng Zhai, Guangtao East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China Minist Educ Key Lab Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781728180687

Indoor navigation is urgently needed by blind people in their everyday lives. In this paper, we design an assistive cane with visual odometry based on actual requirements of the blind to aid them in attaining safe indoor navigation. Compared to the state-of-the-art indoor navigation systems, the proposed device is portable, compact, and adaptable. The main specifications of the system are: the perception range is respectively from 0.10m to 2.10m, and 0.08m to 1.60m for width and length dimensions;the maximum weight is 2.1kg;the detection range is from 0.15m and 3.00m;the cruising ability is about 8h;and the objects whose heights are below 80cm can be detected. The demo video of the proposed navigation system is available at: https://***/10.6084/***.12399572.v1.

关键词： obstacle avoidance indoor navigation visually assistive device

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HDR image Compression with Convolutional Autoencoder

HDR Image Compression with Convolutional Autoencoder

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ieee international conference on visual communications and image processing (vcip)

作者： Han, Fei Wang, Jin Xiong, Ruiqin Zhu, Qing Yin, Baocai Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Peking Univ Inst Digital Media Beijing Peoples R China

ISBN: (纸本)9781728180687

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.

关键词： Convolutional neural networks

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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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Volumetric End-to-End Optimized Compression for Brain images

Volumetric End-to-End Optimized Compression for Brain Images

引用

ieee international conference on visual communications and image processing (vcip)

作者： Gao, Shuo Zhang, Yueyi Liu, Dong Xiong, Zhiwei Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781728180687

The amount of volumetric brain image increases rapidly, which requires a vast amount of resources for storage and transmission, so it's urgent to explore an efficient volumetric compression method. Recent years have witnessed the progress of deep learning-based approaches for two-dimensional (2D) natural image compression, but the field of learned volumetric image compression still remains unexplored. In this paper, we propose the first end-to-end learning framework for volumetric image compression by extending the advanced techniques of 2D image compression to volumetric images. Specifically, a convolutional autoencoder is used to compress 3D image cubes, and the non-local attention models are embedded in the convolutional autoencoder to jointly capture local and global correlations. Both hyperprior and autoregressive models are used to perform the conditional probability estimation in entropy coding. To reduce model complexity, we introduce a convolutional long short-term memory network for the autoregressive model based on channel-wise prediction. Experimental results on volumetric mouse brain images show that the proposed method outperforms JPEG2000-3D, HEVC and state-of-the-art 2D methods.

关键词： Volumetric image compression 3D convolutional neural network convolutional long short-term memory

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3D-CNN Autoencoder for Plenoptic image Compression

3D-CNN Autoencoder for Plenoptic Image Compression

引用

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 Robust Multilinear Mixing Model with l2,1 norm for Unmixing Hyperspectral images

A Robust Multilinear Mixing Model with l2,1 norm for Unmixin...

引用

ieee international conference on visual communications and image processing (vcip)

作者： Li, Minglei Zhu, Fei Guo, Alan J. X. Tianjin Univ Ctr Appl Math Tianjin Peoples R China

ISBN: (纸本)9781728180687

The unmixing of hyperspectral data is a hot topic in the field of r emote s ensing. H owever, in p resence o f various types of noise, especially the noisy channels, the performance of unmixing approaches is seriously deteriorated. To enhance the robustness of the unmixing method is a subject worth studying. This paper presents a robust unmixing method based on the recently- proposed multilinear mixing model, where the l(2,1) norm is adopted in the loss function to suppress the influence of noise. The sparseness of abundance is also considered to improve the parameter estimation. The resulting optimization problem is solved by the alternating direction multiplier method (ADMM). Experiments on both synthetic and real images demonstrate the performance of the proposed unmixing strategy.

关键词： hyperspectral image nonlinear unmixing alternating direction method of multipliers robust l(2,1) norm

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Deep Blind Video Quality Assessment for User Generated Videos

Deep Blind Video Quality Assessment for User Generated Video...

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ieee international conference on visual communications and image processing (vcip)

作者： Tang, Jiapeng Dong, Yu Xie, Rong Gu, Xiao Song, Li Li, Lin Zhou, Bing Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China MIGU Co Ltd Beijing Peoples R China

ISBN: (纸本)9781728180687

As short video industry grows up, quality assessment of user generated videos has become a hot issue. Existing no reference video quality assessment methods are not suitable for this type of application scenario since they are aimed at synthetic videos. In this paper, we propose a novel deep blind quality assessment model for user generated videos according to content variety and temporal memory effect. Content-aware features of frames are extracted through deep neural network, and a patch-based method is adopted to obtain frame quality score. Moreover, we propose a temporal memory-based pooling model considering temporal memory effect to predict video quality. Experimental results conducted on KoNViD-1k and LIVE-VQC databases demonstrate that the performance of our proposed method outperforms other state-of-the-art ones, and the comparative analysis proves the efficiency of o ur t emporal p ooling model.

关键词： video quality assessment user generated videos no reference

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