Hybrid regularizers-based adaptive anisotropic diffusion for image denoising

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Table 4 Comparison results in MS-SSIM for different levels of Gaussian noise

Method	Lena	Peppers	Barbara	Cameraman	House
\(\sigma =5\)
Noise	0.97	0.98	0.98	0.97	0.97
TV model	0.99	0.99	0.99	0.98	0.99
LLT model	0.96	0.99	0.95	0.96	0.97
NLM model	0.98	0.99	0.99	0.98	0.98
BLS-GSM	0.99	0.99	0.98	0.98	0.98
Hybrid model	0.98	0.98	0.98	0.97	0.97
Proposed model	0.99	0.99	0.99	0.99	0.99
\(\sigma =10\)
Noise	0.93	0.95	0.95	0.93	0.92
TV model	0.97	0.98	0.97	0.97	0.97
LLT model	0.96	0.97	0.95	0.96	0.96
NLM model	0.98	0.98	0.98	0.98	0.98
BLS-GSM	0.98	0.98	0.97	0.98	0.98
Hybrid model	0.97	0.97	0.98	0.99	0.98
Proposed model	0.98	0.98	0.98	0.99	0.98
\(\sigma =20\)
Noise	0.83	0.89	0.88	0.83	0.82
TV model	0.95	0.97	0.92	0.93	0.96
LLT model	0.94	0.95	0.92	0.91	0.93
NLM model	0.97	0.97	0.96	0.96	0.97
BLS-GSM	0.96	0.96	0.96	0.95	0.97
Hybrid model	0.96	0.95	0.96	0.95	0.96
Proposed model	0.97	0.96	0.97	0.96	0.96
\(\sigma =40\)
Noise	0.66	0.76	0.73	0.68	0.65
TV model	0.91	0.93	0.87	0.89	0.92
LLT model	0.88	0.89	0.85	0.82	0.85
NLM model	0.93	0.94	0.92	0.93	0.94
BLS-GSM	0.93	0.93	0.91	0.93	0.94
Hybrid model	0.92	0.93	0.90	0.92	0.93
Proposed model	0.93	0.94	0.92	0.94	0.94
\(\sigma =50\)
Noise	0.60	0.70	0.67	0.63	0.60
TV model	0.89	0.91	0.85	0.89	0.90
LLT model	0.84	0.86	0.82	0.80	0.82
NLM model	0.91	0.92	0.89	0.91	0.92
BLS-GSM	0.91	0.92	0.88	0.90	0.92
Hybrid model	0.90	0.91	0.88	0.89	0.91
Proposed model	0.92	0.92	0.90	0.91	0.92