Color Image Denoising Using Clustering

Image processing is any form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or a set of characteristics or parameters related to the image. Image denoising refers to the recovery of a digital image that has been contaminated by additive white Gaussian noise. In Existing a patch-based Wiener filter that exploits patch redundancy for image denoising. It uses both geometrically and photo metrically similar patches to estimate the different filter parameters. And these parameters can be accurately estimated directly from the input noisy image. So the grayscale denoising method can be applied to denoising the color images through such transformations. However such color–space conversions corrupt the noise characteristics. In this proposed system denoising in the RGB color space is performed using K-Means clustering technique. A locally optimal Wiener-filter-based method and have extended it to take advantage of patch redundancy to improve the denoising performance.