Image de-noising by non-local means algorithm

Images are often corrupted with noise during acquisition, transmission and retrieval from storage media. So the need for efficient image de-noising methods has grown with the massive and easy production of digital images and movies. Furthermore, de-noising is often necessary as a pre-processing step in image compression, segmentation, recognition etc. Therefore, de-noising has been an important and widely studied problem in image processing and computer vision. Basically, the image de-noising methods are divided into two types: local and non-local. The methods that only exploit the spatial redundancy in local neighborhoods are referred as Local methods. The methods that estimate pixel intensity based on information from the whole image and thereby exploiting the presence of similar patterns and features in an image are referred as Non-Local. A non local method called as Non-Local Means[3] estimates a noise-free pixel intensity as a weighted average of all pixel intensities in the image, and the weights are proportional to the similarity between the local neighbourhood of the pixel being processed and local neighbourhoods of surrounding pixels. The method is quite spontaneous and powerful that results in comparable PSNR and visual quality to other non-local methods.