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Residual Dilated Network with Attention for Image Blind Denoising

Hou, G; Yang, Y; Xue, J-H; (2019) Residual Dilated Network with Attention for Image Blind Denoising. In: Karam, Lina J and Mei, Tao and Wu, Feng, (eds.) Proceedings of the 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE Xplore: New York, USA. Green open access

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Abstract

Image denoising has recently witnessed substantial progress. However, many existing methods remain suboptimal for texture restoration due to treating different image regions and channels indiscriminately. Also they need to specify the noise level in advance, which largely hinders their use in blind denoising. Therefore, we introduce both attention mechanism and automatic noise level estimation into image denoising. Specifically, we propose a new, effective end-toend attention-embedded neural network for image denoising, named as Residual Dilated Attention Network (RDAN). Our RDAN is composed of a series of tailored Residual Dilated Attention Blocks (RDAB) and Residual Conv Attention Blocks (RCAB). The RDAB and RCAB incorporates both non-local and local operations, which enable a comprehensive capture of structural information. In addition, we incorporate a Gaussian-based noise level estimation into RDAN to accomplish blind denoising. Experimental results have demonstrated that our RDAN can substantially outperforms the state-of-the-art denoising methods as well as promisingly preserve image texture.

Type: Proceedings paper
Title: Residual Dilated Network with Attention for Image Blind Denoising
ISBN: 978-1-5386-9552-4
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/icme.2019.00051
Publisher version: https://doi.org/10.1109/ICME.2019.00051
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Noise reduction, Noise level, Image denoising, Estimation, Image restoration, Convolution, Noise measurement, Attention mechanism, Image blind denoising, Non-local operation
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10079665
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