Multi-stage image denoising with the wavelet transform

TL;DR

This paper introduces a multi-stage CNN for image denoising that combines wavelet transforms and dynamic convolutional blocks to improve denoising performance while managing computational costs.

Contribution

The paper proposes a novel multi-stage CNN architecture with wavelet transforms and dynamic convolutional blocks for enhanced image denoising.

Findings

Outperforms popular denoising methods in quantitative metrics

Effectively recovers detailed information in noisy images

Reduces redundant features for better denoising quality

Abstract

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.