OCT image Denoising Based On K-SVD Method
Journal Title: GRD Journal for Engineering - Year 2016, Vol 1, Issue 0
Abstract
Enhancement of the images will be more helpful in surveillances and remote sensing applications. While increasing the size of the image the original image quality will be affected. In order to avoid the loss of quality while enhancing the image, Non Local Means (NLM) Optimized Sparse Method is used. The noises and the pixel differences occurring in the up sampling and down sampling of the images were identified and they were removed based on the proposed method. Finally the images were enhanced by the edge enhancement process. The performance of the proposed method is proved using the performance parameters. The input OCT images were taken as input. Noises were added to the input images producing a noisy images. The input images were denoised based on the K-SVD process. K-SVD is a generalization of the k-means clustering method, and it works by iteratively alternating between sparse coding the input data based on the current dictionary, and updating the atoms in the dictionary to better fit the data. It aims to partition n observations into few clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. The denoised images were then clustered and edges of the clusters were identified. The clusters were estimated based on the FCM clustering process. The performance of the process is then measured using the performance parameters like PSNR, MSE and SSIM.
Authors and Affiliations
A. Priyanga, V. Nandhini Devi, M. Tamilarasi, Dr. S. Venkatanarayanan
Keywords
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