Multi-stage image denoising with the wavelet transform
Chunwei Tian, Menghua Zheng, Wangmeng Zuo, Bob Zhang, Yanning Zhang,, David Zhang

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…
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Taxonomy
TopicsImage and Signal Denoising Methods · Advanced Image Fusion Techniques · Photoacoustic and Ultrasonic Imaging
MethodsBatch Normalization · *Communicated@Fast*How Do I Communicate to Expedia? · Convolution · Residual Connection · Residual Block
