Hybrid regularizers-based adaptive anisotropic diffusion for image denoising

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Table 5 Comparison results in FSIM for different levels of Gaussian noise

Method	Lena	Peppers	Barbara	Cameraman	House
\(\sigma =5\)
Noise	0.99	0.96	0.99	0.95	0.95
TV model	0.99	0.97	0.99	0.97	0.97
LLT model	0.99	0.97	0.99	0.97	0.97
NLM model	0.99	0.98	0.99	0.98	0.97
BLS-GSM	0.99	0.97	0.99	0.98	0.97
Hybrid model	0.99	0.97	0.98	0.97	0.96
Proposed model	0.99	0.99	0.99	0.98	0.98
\(\sigma =10\)
Noise	0.95	0.87	0.96	0.86	0.86
TV model	0.98	0.95	0.96	0.93	0.93
LLT model	0.97	0.94	0.97	0.93	0.93
NLM model	0.98	0.97	0.98	0.95	0.95
BLS-GSM	0.98	0.96	0.97	0.95	0.94
Hybrid model	0.97	0.95	0.96	0.94	0.94
Proposed model	0.98	0.96	0.97	0.96	0.95
\(\sigma =20\)
Noise	0.86	0.73	0.88	0.72	0.71
TV model	0.95	0.91	0.92	0.88	0.88
LLT model	0.94	0.90	0.92	0.86	0.88
NLM model	0.96	0.94	0.94	0.91	0.92
BLS-GSM	0.95	0.93	0.94	0.90	0.91
Hybrid model	0.94	0.92	0.93	0.89	0.91
Proposed model	0.96	0.93	0.94	0.91	0.92
\(\sigma =40\)
Noise	0.72	0.54	0.74	0.56	0.53
TV model	0.93	0.86	0.89	0.81	0.84
LLT model	0.91	0.81	0.86	0.76	0.82
NLM model	0.93	0.89	0.92	0.85	0.88
BLS-GSM	0.92	0.88	0.92	0.84	0.87
Hybrid model	0.92	0.87	0.91	0.84	0.87
Proposed model	0.94	0.89	0.92	0.85	0.88
\(\sigma =50\)
Noise	0.65	0.48	0.67	0.51	0.48
TV model	0.89	0.83	0.87	0.79	0.82
LLT model	0.86	0.78	0.84	0.73	0.79
NLM model	0.91	0.88	0.90	0.83	0.85
BLS-GSM	0.90	0.87	0.89	0.82	0.85
Hybrid model	0.89	0.86	0.88	0.81	0.84
Proposed model	0.91	0.88	0.90	0.82	0.85