Dual-Core Adaptive NLM Image Denoising Algorithm Based on Variable-Size Window and Neighborhood Multifeatures

Table 2

SSIM table for different simulation results.

Image	σ	Noisy image	Traditional NLM	Gaussian kernel NLM	Literature [12] method	Literature [13] method	Literature [14] method	Literature [15] method	Algorithms in this paper
jetplane	20	0.637	0.804	0.851	0.804	0.851	0.804	0.851	0.832
	40	0.601	0.761	0.768	0.771	0.781	0.791	0.799	0.815
	60	0.572	0.623	0.675	0.683	0.715	0.723	0.745	0.781
C.man	20	0.613	0.685	0.728	0.785	0.798	0.815	0.828	0.816
	40	0.581	0.628	0.631	0.648	0.651	0.678	0.731	0.768
	60	0.532	0.605	0.621	0.635	0.639	0.655	0.661	0.679
Boat	20	0.623	0.704	0.725	0.734	0.765	0.784	0.825	0.814
	40	0.564	0.631	0.652	0.671	0.692	0.711	0.752	0.792
	60	0.528	0.611	0.626	0.631	0.656	0.661	0.676	0.687
Lax	20	0.625	0.636	0.662	0.686	0.712	0.736	0.762	0.751
	40	0.579	0.629	0.648	0.659	0.668	0.689	0.718	0.732
	60	0.531	0.579	0.611	0.639	0.641	0.651	0.661	0.665
Lena	20	0.612	0.713	0.732	0.748	0.772	0.786	0.827	0.821
	40	0.599	0.635	0.661	0.679	0.702	0.718	0.749	0.789
	60	0.524	0.612	0.624	0.641	0.648	0.657	0.671	0.692
Pepper	20	0.619	0.698	0.718	0.729	0.771	0.792	0.831	0.824
	40	0.576	0.641	0.652	0.661	0.672	0.686	0.724	0.774
	60	0.511	0.603	0.621	0.638	0.652	0.667	0.681	0.698