Improved preclassification non local-means (IPNLM) for filtering of grayscale images degraded with additive white Gaussian noise

In this paper, we develop an extensive research on different types of grayscale images applying standard non local (NL)-means algorithm on different search and patch windows sizes to obtain optimal parameters where the values of criteria peak signal-to-noise ratio (PSNR), mean absolute error (MAE), and structural similarity index (SSIM) would be the best possible. The research shows quantitatively the importance on the appropriate selection of the windows sizes used during the filtering process. Based on the optimal parameters of the standard NL-means, we propose the improved preclassification non local-means (IPNLM) for filtering grayscale images degraded with additive white Gaussian noise (AWGN). The proposal uses a descriptors evaluation for each search window in the noisy image to apply statistical neighborhood preclassification respect to the homogeneity of each window to distinguish whether the current noisy pixel is in a homogeneous region or it is in an edge object region. Also, two thresholds based on the standard deviation of the local region in the noisy image are proposed to classify the pixels and perform a filtering level degree providing a commitment between the image denoising and the processing time. The proposal IPNLM reveals good results outperforming other filters based on NL-means by balancing the tradeoff between the noise suppression, detail preservation, and processing time. Experimental results demonstrate that IPNLM algorithm can reduce considerably the processing time from 8 through 15 times in comparison with the standard NL-means and other analyzed filters.