EURASIP Journal on Advances in Signal Processing

Table 6 SSIM comparison for Gaussian blur plus Gaussian noise

From: A collaborative adaptive Wiener filter for image restoration using a spatial-domain multi-patch correlation model

Image	Method	SSIM
		Scenario	Scenario	Scenario	Scenario
		I	II	III	IV
Aerial	Corrupted	0.8891	0.7794	0.8176	0.7117
491×434	L0-Abs	0.9087	0.8050	0.7821	0.6448
	TVMM	0.9215	0.8325	0.7885	0.6604
	BM3DDEB	0.9198	0.8571	0.8400	0.7710
	IDD-BM3D	0.9292	0.8591	0.8406	0.7577
	AWF	0.9199	0.8519	0.8517	0.7733
	CAWF	0.9273	0.8662	0.8649	0.7957
Bridge	Corrupted	0.8836	0.7975	0.7745	0.6929
512²	L0-Abs	0.8902	0.8044	0.7425	0.6524
	TVMM	0.8995	0.8156	0.7323	0.6689
	BM3DDEB	0.9099	0.8579	0.8294	0.7796
	IDD-BM3D	0.9173	0.8573	0.8252	0.7657
	AWF	0.9080	0.8503	0.8344	0.7712
	CAWF	0.9155	0.8637	0.8462	0.7892
River	Corrupted	0.8373	0.7198	0.7246	0.6161
(Kodak 11)	L0-Abs	0.8328	0.7159	0.6670	0.5560
768×512	TVMM	0.8452	0.7219	0.6344	0.5421
	BM3DDEB	0.8487	0.7713	0.7377	0.6745
	IDD-BM3D	0.8672	0.7767	0.7463	0.6666
	AWF	0.8573	0.7736	0.7676	0.6878
	CAWF	0.8707	0.7949	0.7837	0.7105
Bones	Corrupted	0.8231	0.7323	0.6681	0.5884
512×768	L0-Abs	0.8051	0.7287	0.6652	0.6181
	TVMM	0.7954	0.7192	0.6427	0.6172
	BM3DDEB	0.8320	0.7768	0.7420	0.7064
	IDD-BM3D	0.8421	0.7753	0.7389	0.6969
	AWF	0.8358	0.7699	0.7526	0.7000
	CAWF	0.8476	0.7866	0.7615	0.7108
Building	Corrupted	0.8119	0.7162	0.6609	0.5753
768×512	L0-Abs	0.8356	0.7491	0.7142	0.6397
	TVMM	0.8454	0.7592	0.6936	0.6615
	BM3DDEB	0.8520	0.7913	0.7633	0.7138
	IDD-BM3D	0.8662	0.7953	0.7715	0.7130
	AWF	0.8509	0.7848	0.7721	0.7147
	CAWF	0.8607	0.7974	0.7816	0.7268
Gazebo	Corrupted	0.8279	0.7413	0.6819	0.6030
768×512	L0-Abs	0.8651	0.7867	0.7527	0.6788
	TVMM	0.8741	0.8057	0.7785	0.6826
	BM3DDEB	0.8805	0.8249	0.8001	0.7500
	IDD-BM3D	0.8898	0.8282	0.8064	0.7504
	AWF	0.8796	0.8190	0.8035	0.7481
	CAWF	0.8801	0.8238	0.8072	0.7561

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