An Image Denoising Method Based on BM4D and GAN in 3D Shearlet Domain

Table 4

The results of the different methods for Gaussian noise.

PSNR SSIM EPI	Filter	/%
		1	3	5	7	9	11	13	15	17	19
Gauss noise	Noisy data	40.00	30.46	26.02	23.10	20.91	19.17	17.72	16.48	15.39	14.42
		0.97	0.81	0.66	0.53	0.43	0.36	0.30	0.25	0.22	0.19
		0.20	0.19	0.17	0.17	0.14	0.13	0.10	0.09	0.06	0.04
	OB-NLM3D	42.47	37.57	34.73	32.82	31.42	30.32	29.40	28.61	27.91	27.28
		0.99	0.97	0.95	0.92	0.90	0.87	0.84	0.82	0.79	0.77
		0.29	0.28	0.26	0.25	0.23	0.20	0.19	0.17	0.15	0.14
	OB-NLM3D-WM	42.52	37.75	35.01	33.13	31.73	30.61	29.68	28.88	28.18	27.55
		0.99	0.97	0.95	0.93	0.90	0.88	0.85	0.83	0.80	0.78
		0.32	0.30	0.29	0.28	0.26	0.26	0.24	0.22	0.20	0.18
	ODCT3D	43.78	37.53	34.89	33.18	31.91	30.90	30.07	29.35	28.73	28.18
		0.99	0.97	0.95	0.93	0.91	0.89	0.88	0.86	0.85	0.83
		0.47	0.46	0.45	0.42	0.40	0.37	0.36	0.31	0.28	0.25
	PRI-NLM3D	44.04	38.26	35.51	33.67	32.37	31.29	30.40	29.65	28.99	28.40
		0.99	0.98	0.96	0.94	0.92	0.90	0.89	0.87	0.85	0.84
		0.66	0.64	0.62	0.60	0.59	0.56	0.55	0.55	0.53	0.51
	BM4D	44.09	38.39	35.95	34.38	33.21	32.28	31.50	30.82	30.23	29.70
		0.99	0.98	0.96	0.95	0.93	0.92	0.91	0.90	0.88	0.87
		0.70	0.69	0.68	0.67	0.60	0.58	0.56	0.56	0.55	0.54
	Proposed	45.08	39.31	36.79	35.12	33.79	33.31	32.16	31.58	30.79	30.53
		0.99	0.97	0.96	0.95	0.93	0.92	0.91	0.90	0.88	0.88
		0.75	0.72	0.68	0.67	0.63	0.61	0.60	0.59	0.57	0.55