This paper as well as Multi-stage image denoising with the wavelet transform is conducted by Chunwei Tian, Menghua Zheng, Wangmeng Zuo, Bob Zhang, Yanning Zhang, David Zhang. It is accepted by the Pattern Recognition (SCI-IF:8.518) in 2022. It can be obtained at https://arxiv.org/abs/2209.12394.

This paper uses a dynamic convolution, wavelet transform and residual dense architecture to implment an efficient CNN for addressing image denoising under complex scenes.

MWDCNN_out.mp4

Its abstract is shown as follows.

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.

Requirements (Pytorch)

Pytorch 1.10.2

Python 3.8

openCv for Python

pytorch_wavelets

tqdm

skimage

DataSet

Training datasets

The training dataset of the gray noisy images is downloaded at https://pan.baidu.com/s/1nkY-b5_mdzliL7Y7N9JQRQ or https://drive.google.com/open?id=1_miSC9_luoUHSqMG83kqrwYjNoEus6Bj (google drive)

The training dataset of the color noisy images is downloaded at https://pan.baidu.com/s/1ou2mK5JUh-K8iMu8-DMcMw (baiduyun) or https://drive.google.com/open?id=1S1_QrP-fIXeFl5hYY193lr07KyZV8X8r (google drive)

Test dataset of Set68 is downloaded at https://drive.google.com/file/d/1_fw6EKne--LVnW0mo68RrIY-j6BKPdSp/view?usp=sharing (google drive)

Test dataset of Set12 is downloaded at https://drive.google.com/file/d/1cpQwFpNv1MXsM5bJkIumYfww8EPtlkWf/view?usp=sharing (google drive)

Test dataset of CBSD68 is downloaded at https://drive.google.com/file/d/1lxXQ_buMll_JVWxKpk5fp0jduW5F_MHe/view?usp=sharing (google drive)

Test dataset of Kodak24 is downloaded at https://drive.google.com/file/d/1F4_mv4oTXhiG-zyG9DI4OO05KqvEKhs9/view?usp=sharing (google drive)

The training dataset of real noisy images is downloaded at https://drive.google.com/file/d/1IYkR4zi76p7O5OCevC11VaQeKx0r1GyT/view?usp=sharing and https://drive.google.com/file/d/19MA-Rgfc89sW9GJHpj_QedFyo-uoS8o7/view?usp=sharing （google drive）

The test dataset of real noisy images is downloaded at https://drive.google.com/file/d/17DE-SV85Slu2foC0F0Ftob5VmRrHWI2h/view?usp=sharing (google drive)

Command

Train MWDCNN-S (WMDCNN with know noise level)

python train.py --mode train --n_pat_per_image 512 --batch_size 64 --model_name mwdcnn --rgb_range 1 --n_colors 1 --sigma 25 --lr 1e-4 --n_GPUs 1 --GPU_ id 0

Train MWDCNN-B (MWDCNN with blind nosie level)

python train.py --mode train --n_pat_per_image 512 --batch_size 64 --model_name mwdcnn --rgb_range 1 --n_colors 1 --sigma 100 --lr 1e-4 --n_GPUs 1 --GPU_ id 0

Test

cd main

Color blind denoising

python train.py --model_file_name ../model_zoo/MWDCNN/real/model_sigma200.pth --mode test --test_dataset real_dataset --model_name mwdcnn --rgb_range 1 --n_GPUs 1 --GPU_id 1 --n_colors 3 --sigma 200

1. Network architecture of WMDCNN.

2. Architecture of weight generator (WG)

3. Architecture of feature enhancement (FE) as well as residual dense block (RDB)

4. Average PSNR (dB) of different methods on BSD68 with noise levels of 15, 25 and 50.

5. Average PSNR (dB) of different methods on Set12 with noise levels of 15, 25 and 50.

6. Average PSNR (dB) of different methods on CBSD68 with noise levels of 15, 25, 35, 50 and 75.

7. Average PSNR (dB) of different methods on Kodak24 with noise levels of 15, 25, 35, 50 and 75.

8. PSNR (dB) of different methods on real noisy images (CC).

9. FSIM results of different methods on BSD68 with noise levels of 15, 25 and 50.

10. FSIM results of different methods on CBSD68 and Kodak24 with noise levels of 15, 25, 35, 50 and 75.

11. SSIM results of different methods on Kodak24 with noise levels of 15, 25, 35, 50 and 75.

12. LPIPS results of different methods on Kodak24 with noise levels of 15, 25, 35, 50 and 75.

13. Inception-Score of different methods on Kodak24 with noise levels of 15 and 50.

14. Visual results of Set12 with noise level of 15.

15. Visual results of Set12 with noisel level of 25.

16. Visual results of Kodak24 with noise level of 25.

17. Visual results of Kodak24 with noise level of 35.

You can cite this paper by the following ways.

[1] C. Tian, M. Zheng, W. Zuo, B. Zhang, Y. Zhang, D. Zhang. Multi-stage image denoising with the wavelet transform [J]. Pattern Recogition. 2023.