An FCM-Based Image De-Noising with Spatial Statistics Pilot Study

Abstract

Image de-noising is an important scheme that makes an image visually prominent and obtains enough useful information to produce a clear image. Many applications have been developed for effective noise suppression that produce good image quality. This study assumed that a residual image consisted of noise with edges produced by subtracting the original image with a low-pass-filter-smoothed image. The Moran statistics were then used to measure the variation in spatial information in residual images and we then used this information as feature data input into the Fuzzy C-means (FCM) algorithm. Three clusters were pre-assumed for FCM in this work: they were heavy, medium, and less noisy areas. The rates for each position partially belonged to each cluster determined using an FCM membership function. Each pixel in a noisy image was assumed in de-noising processing as a linear combination of the product of three de-noised images with membership functions in the same position. Average filters with different windows and a Gaussian filter were a priori applied to this noisy image to create three de-noised versions. The results showed that this scheme worked better than the non-adaptive smoothing. This scheme‘s performance was evaluated and compared to the bilateral filter and non-local means (NLM) using the peak signal to noise ratio (PSNR) and structure similarity index measure (SSIM). The developed scheme is a pilot study. Further future studies are needed on the optimized number of clusters and smoother versions used in linear combination.