Multi-stage image denoising with the wavelet transform

• Published in: Pattern recognition Vol. 134; p. 109050
• Main Authors: Tian, Chunwei, Zheng, Menghua, Zuo, Wangmeng, Zhang, Bob, Zhang, Yanning, Zhang, David
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2023
• Subjects: CNN; Dynamic convolution; Image denoising; Signal processing; Wavelet transform; Signal processing; Dynamic convolution; CNN; Wavelet transform; Image denoising
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2022.109050

•A dynamic convolution is used into a CNN to address limitations in depth and width of lightweight CNNs for pursuing good denoising performance.•The combination of a signal processing technique and discriminative learning technique is used for image denoising.•Enhanced residual dense architectures are used to remove redundant information for improving denoising effects. Deep convolutional neural networks (CNNs) are used for image denoising via automatically mining accurate structure information. However, most of existing CNNs depend on enlarging depth of designed networks to obtain better denoising performance, which may cause training difficulty. In this paper, we propose a multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three stages, i.e., a dynamic convolutional block (DCB), two cascaded wavelet transform and enhancement blocks (WEBs) and a residual block (RB). DCB uses a dynamic convolution to dynamically adjust parameters of several convolutions for making a tradeoff between denoising performance and computational costs. WEB uses a combination of signal processing technique (i.e., wavelet transformation) and discriminative learning to suppress noise for recovering more detailed information in image denoising. To further remove redundant features, RB is used to refine obtained features for improving denoising effects and reconstruct clean images via improved residual dense architectures. Experimental results show that the proposed MWDCNN outperforms some popular denoising methods in terms of quantitative and qualitative analysis. Codes are available at https://github.com/hellloxiaotian/MWDCNN.