检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Tian, Chunwei Zhuge, Ruibin Wu, Zhihao Xu, Yong Zuo, Wangmeng Chen, Chen Lin, Chia-Wen Bio-Computing Research Center Harbin Institute of Technology Shenzhen ShenzhenGuangdong518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition ShenzhenGuangdong518055 China Peng Cheng Laboratory ShenzhenGuangdong518055 China School of Computer Science and Technology Harbin Institute of Technology HarbinHeilongjiang150001 China Department of Electrical and Computer Engineering University of North Carolina CharlotteNC28223 United States Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan

Deep convolutional neural networks (CNNs) with strong expressive ability have achieved impressive performances on single image super-resolution (SISR). However, their excessive amounts of convolutions and parameters usually consume high computational cost and more memory storage for training a SR model, which limits their applications to SR with resource-constrained devices in real world. To resolve these problems, we propose a lightweight enhanced SR CNN (LESRCNN) with three successive sub-blocks, an information extraction and enhancement block (IEEB), a reconstruction block (RB) and an information refinement block (IRB). Specifically, the IEEB extracts hierarchical low-resolution (LR) features and aggregates the obtained features step-by-step to increase the memory ability of the shallow layers on deep layers for SISR. To remove redundant information obtained, a heterogeneous architecture is adopted in the IEEB. After that, the RB converts low-frequency features into high-frequency features by fusing global and local features, which is complementary with the IEEB in tackling the long-term dependency problem. Finally, the IRB uses coarse high-frequency features from the RB to learn more accurate SR features and construct a SR image. The proposed LESRCNN can obtain a high-quality image by a model for different scales. Extensive experiments demonstrate that the proposed LESRCNN outperforms state-of-the-arts on SISR in terms of qualitative and quantitative evaluation. The code of LESRCNN is accessible on https://***/hellloxiaotian/LESRCNN. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Lightweight Image Super-Resolution with Enhanced CNN

arXiv

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arXiv 2020年

作者： Tian, Chunwei Zhuge, Ruibin Wu, Zhihao Xu, Yong Zuo, Wangmeng Chen, Chen Lin, Chia-Wen Bio-Computing Research Center Harbin Institute of Technology Shenzhen ShenzhenGuangdong518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition ShenzhenGuangdong518055 China Peng Cheng Laboratory ShenzhenGuangdong518055 China School of Computer Science and Technology Harbin Institute of Technology HarbinHeilongjiang150001 China Department of Electrical and Computer Engineering University of North Carolina at Charlotte NC28223 United States Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan

关键词： Convolutional neural networks

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Designing and Training of A Dual CNN for Image Denoising

arXiv

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arXiv 2020年

作者： Zuo, Wangmeng Zhang, David Lin, Chia-Wen Xu, Yong Tian, Chunwei Du, Bo Bio-Computing Research Center Harbin Institute of Technology Shenzhen China Shenzhen Key Laboratory of Visual Object Detection and Recognition ShenzhenGuangdong518055 China Peng Cheng Laboratory Shenzhen518055 China School of Computer Science and Technology Harbin Institute of Technology 150001 HarbinHeilongjiang China Peng Cheng Laboratory Shenzhen518055 China School of Computer Science Wuhan University 430072 WuhanHubei China Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan 518172 ShenzhenGuangdong China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Deep convolutional neural networks (CNNs) for image denoising have recently attracted increasing research interest. However, plain networks cannot recover fine details for a complex task, such as real noisy images. In this paper, we propose a Dual denoising Network (DudeNet) to recover a clean image. Specifically, DudeNet consists of four modules: a feature extraction block, an enhancement block, a compression block, and a reconstruction block. The feature extraction block with a sparse mechanism extracts global and local features via two sub-networks. The enhancement block gathers and fuses the global and local features to provide complementary information for the latter network. The compression block refines the extracted information and compresses the network. Finally, the reconstruction block is utilized to reconstruct a denoised image. The DudeNet has the following advantages: (1) The dual networks with a parse mechanism can extract complementary features to enhance the generalized ability of denoiser. (2) Fusing global and local features can extract salient features to recover fine details for complex noisy images. (3) A Small-size filter is used to reduce the complexity of denoiser. Extensive experiments demonstrate the superiority of DudeNet over existing current state-of-the-art denoising methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Recovery

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Deep learning on image denoising: An overview

arXiv

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arXiv 2019年

作者： Tian, Chunwei Fei, Lunke Zheng, Wenxian Xu, Yong Zuo, Wangmeng Lin, Chia-Wen Bio-Computing Research Center Harbin Institute of Technology Shenzhen Shenzhen Guangdong518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen Guangdong518055 China School of Computers Guangdong University of Technology Guangzhou Guangdong510006 China Tsinghua Shenzhen International Graduate School Shenzhen Guangdong518055 China Peng Cheng Laboratory Shenzhen Guangdong518055 China School of Computer Science and Technology Harbin Institute of Technology Harbin Heilongjiang150001 China Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan

Deep learning techniques have obtained much attention in image denoising. However, deep learning methods of different types deal with the noise have enormous differences. Specifically, discriminative learning based on deep learning can well address the Gaussian noise. Optimization model methods based on deep learning have good effect on estimating of the real noise. So far, there are little related researches to summarize different deep learning techniques for image denoising. In this paper, we make such a comparative study of different deep techniques in image denoising. We first classify the (1) deep convolutional neural networks (CNNs) for additive white noisy images, (2) deep CNNs for real noisy images, (3) deep CNNs for blind denoising and (4) deep CNNs for hybrid noisy images, which is the combination of noisy, blurred and low-resolution images. Then, we analyze the motivations and principles of deep learning methods of different types. Next, we compare and verify the state-of-the-art methods on public denoising datasets in terms of quantitative and qualitative analysis. Finally, we point out some potential challenges and directions of future research. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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