A Denoising Framework with a ROR Mechanism Using FCM Clustering Algorithm and NLM

-Impulse noise detection is a critical issue when removing impulse noise and impulse/gaussian mixed noise. The framework combines Robust Outlyingness Ratio (ROR) detection mechanism and Fuzzy C Means (FCM) clustering algorithm and Nonlocal Means (NLM) filter. ROR for measuring how impulse like each pixel is and then all pixels are divided into four clusters according to the ROR values. The detection mechanism consists of coarse and fine stage. The output of coarse and fine stage of ROR is passed to FCM in which we use an iterative approach where each data point belongs to one or more centroids. Hence by iteratively updating the cluster centers and the membership grades for each data point, FCM iteratively moves the cluster centers to the "right" location within a data set and the image is fine tuned and finally the fine tuned image is passed through NLM filter in which the image is denoised and the output is produced. An overview of a mechanism is to improve image quality in terms of PSNR ratio, through FCM mechanism. Thus it will infer a high quality image compared to other filtering techniques and also increases PSNR ratio. The main objective of this project is to remove noise from input image and improving the efficiency and the visual quality of the image. Keywords-Image denoising, impulse noise, Gaussian mixed noise, Robust Outlyingness Ratio (ROR), Fuzzy C Means (FCM) clustering algorithm, Nonlocal Means (NLM), Peak Signal to Noise Ratio (PSNR).