Description

Image denoising is applied in display systems to improve the quality of image. The natural or texture images contain repetitive patterns.One of the popular denoising methods, NLM, produces the quality of image compared than other denoising methods. The main concept is to replace each pixel with a weighted average of other pixels with similar neighbor-hoods, which dependes on intensity values of an image. It is very expensive for calculating the similarity pixels of image. We propose to improve non local means by integrating Gaussian blur, clustering, and rotationally invariant block matching (RIBM) into the NLM framework.We performed clustering based on pre classification and applied RIBM to each clusters and calculated the weighted average of the pixels for producing good quality of image.