Multi-stage image denoising with the wavelet transform

作者：

Highlights：

• 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.

摘要

•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.

论文关键词：Image denoising,CNN,Wavelet transform,Dynamic convolution,Signal processing

论文评审过程：Received 3 May 2022, Revised 17 September 2022, Accepted 20 September 2022, Available online 21 September 2022, Version of Record 7 October 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.109050