DnCNN

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Main Contents

TrainingCodes: training demo for Gaussian denoising.

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 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 BSD68 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