A Novel Approach for Graph – Based Segmentation of Image Denoising

Image Denoising is a major research topic for many image processing researchers. In some image Denoising algorithms, the user is required to specify the number of smooth image regions to estimate the noise level. In order to overcome this difficulty, we adopt a segmentation-based approach to estimate the noise level from a single image. The image is partitioned into smooth regions, in which the mean is the estimate of brightness, and the standard deviation is an overestimate of noise level, we specify a function called ‘noise level function’ (NLF) [1]. After estimating the noise, Denoising can be done by using Gaussian Conditional Random Field (GCRF). Extensive experiments were conducted to test the proposed algorithm, which is chosen to outperform state-of-the-art Denoising algorithms.