Image denoising based on deep residual network with dual-domain information

doi:10.3969/j.issn.1000-1565.2023.02.014

Abstract

Abstract: The denoising technology based on deep learning improves the image quality by considering visual artifacts and overall smoothing noise. However, they rarely involve the restoration of edge details. In this paper, a deep denoising network model based on dual-domain mapping and residual learning is proposed. The network learning information is extended by the fusion of wavelet domain information and spatial domain information. By introducing multi-scale learning and dilated convolution into the activation unit, the image features are extracted and the network parameters are reduced. In order to further improve the denoising results, the joint loss function of wavelet domain loss and spatial domain loss are combined to guide the network to obtain more edges and details. Experimental results show that the proposed method can not only effectively remove image noise, but also better restore image texture details, and obtain better results in both subjective and objective evaluation.

Key words: image denoising, dual-domain mapping, residual learning, activation unit, loss function

CLC Number:

TP391

LI Kai, ZHANG Hui, CUI Lijuan, PENG Jinjia, CHEN Taixi. Image denoising based on deep residual network with dual-domain information[J]. Journal of Hebei University(Natural Science Edition), 2023, 43(2): 216-224.

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