基于Matlab实现传统图像去噪算法和深度卷积神经网络的DnCNN图像去噪算法（源码+数据+说明文档）.rar

## Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising ### Main Contents **demos**: `Demo_test_DnCNN-.m`. **model**: including the trained models for Gaussian denoising; a single model for Gaussian denoising, single image super-resolution (SISR) and deblocking. **testsets**: BSD68 and Set10 for Gaussian denoising evaluation; Set5, Set14, BSD100 and Urban100 datasets for SISR evaluation; Classic5 and LIVE1 for JPEG image deblocking evaluation. To run the testing demos `Demo_test_DnCNN-.m`, you should first [install](http://www.vlfeat.org/matconvnet/install/) [MatConvNet](http://www.vlfeat.org/matconvnet/). Note: If you did not install MatConvNet, just replace `res = vl_simplenn(net,input,[],[],'conserveMemory',true,'mode','test')` with `res = simplenn_matlab(net, input)`. ### Results #### Gaussian Denoising The average PSNR(dB) results of different methods on the BSD6868 dataset. | Noise Level | BM3D | WNNM | EPLL | MLP | CSF |TNRD | DnCNN-S | DnCNN-B | |:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:| | 15 | 31.07 | 31.37 | 31.21 | - | 31.24 | 31.42 | **31.73** | **31.61** | | 25 | 28.57 | 28.83 | 28.68 | 28.96 | 28.74 | 28.92 | **29.23** | **29.16** | | 50 | 25.62 | 25.87 | 25.67 | 26.03 | - | 25.97 | **26.23** | **26.23** | #### Gaussian Denoising, Single ImageSuper-Resolution and JPEG Image Deblocking via a Single (DnCNN-3) Model Average PSNR(dB)/SSIM results of different methods for Gaussian denoising with noise level 15, 25 and 50 on BSD68 dataset, single image super-resolution with upscaling factors 2, 3 and 40 on Set5, Set14, BSD100 and Urban100 datasets, JPEG image deblocking with quality factors 10, 20, 30 and 40 on Classic5 and LIVE11 datasets. ###### Gaussian Denoising | Dataset | Noise Level | BM3D | TNRD | DnCNN-3 | |:---------:|:---------:|:---------:|:---------:|:---------:| | | 15 | 31.08 / 0.8722 | 31.42 / 0.8826 | 31.46 / 0.8826 | | BSD68 | 25 | 28.57 / 0.8017 | 28.92 / 0.8157 | 29.02 / 0.8190 | | | 50 | 25.62 / 0.6869 | 25.97 / 0.7029 | 26.10 / 0.7076 | ###### Single Image Super-Resolution | Dataset | Upscaling Factor | TNRD | VDSR |DnCNN-3| |:---------:|:---------:|:---------:|:---------:|:---------:| | | 2 | 36.86 / 0.9556 | 37.56 / 0.9591 | 37.58 / 0.9590 | |Set5 | 3 | 33.18 / 0.9152 | 33.67 / 0.9220 | 33.75 / 0.9222 | | | 4 | 30.85 / 0.8732 | 31.35 / 0.8845 | 31.40 / 0.8845 | || | | 2 | 32.51 / 0.9069 | 33.02 / 0.9128 | 33.03 / 0.9128 | |Set14 | 3 | 29.43 / 0.8232 | 29.77 / 0.8318 | 29.81 / 0.8321 | | | 4 | 27.66 / 0.7563 | 27.99 / 0.7659 | 28.04 / 0.7672 | || | | 2 | 31.40 / 0.8878 | 31.89 / 0.8961 | 31.90 / 0.8961 | |BSD100 | 3 | 28.50 / 0.7881 | 28.82 / 0.7980 | 28.85 / 0.7981 | | | 4 | 27.00 / 0.7140 | 27.28 / 0.7256 | 27.29 / 0.7253 | || | | 2 | 29.70 / 0.8994 | 30.76 / 0.9143 | 30.74 / 0.9139 | |Urban100| 3 | 26.42 / 0.8076 | 27.13 / 0.8283 | 27.15 / 0.8276 | | | 4 | 24.61 / 0.7291 | 25.17 / 0.7528 | 25.20 / 0.7521 | ###### JPEG Image Deblocking | Dataset | Quality Factor | AR-CNN | TNRD | DnCNN-3 | |:---------:|:---------:|:---------:|:---------:|:---------:| |Classic5| 10 | 29.03 / 0.7929 | 29.28 / 0.7992 | 29.40 / 0.8026 | | | 20 | 31.15 / 0.8517 | 31.47 / 0.8576 | 31.63 / 0.8610 | | | 30 | 32.51 / 0.8806 | 32.78 / 0.8837 | 32.91 / 0.8861 | | | 40 | 33.34 / 0.8953 | - | 33.77 / 0.9003 | || | LIVE1 | 10 | 28.96 / 0.8076 | 29.15 / 0.8111 | 29.19 / 0.8123 | | | 20 | 31.29 / 0.8733 | 31.46 / 0.8769 | 31.59 / 0.8802 | | | 30 | 32.67 / 0.9043 | 32.84 / 0.9059 | 32.98 / 0.9090 | | | 40 | 33.63 / 0.9198 | - | 33.96 / 0.9247 |