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http://dx.doi.org/10.7471/ikeee.2019.23.3.910

Adaptive Clustering based Sparse Representation for Image Denoising  

Kim, Seehyun (Dept. of Information and Communications Engineering, The University of Suwon)
Publication Information
Journal of IKEEE / v.23, no.3, 2019 , pp. 910-916 More about this Journal
Abstract
Non-local similarity of natural images is one of highly exploited features in various applications dealing with images. Unique edges, texture, and pattern of the images are frequently repeated over the entire image. Once the similar image blocks are classified into a cluster, representative features of the image blocks can be extracted from the cluster. The bigger the size of the cluster is the better the additive white noise can be separated. Denoising is one of major research topics in the image processing field suppressing the additive noise. In this paper, a denoising algorithm is proposed which first clusters the noisy image blocks based on similarity, extracts the feature of the cluster, and finally recovers the original image. Performance experiments with several images under various noise strengths show that the proposed algorithm recovers the details of the image such as edges, texture, and patterns while outperforming the previous methods in terms of PSNR in removing the additive Gaussian noise.
Keywords
K-means clustering; adaptive clustering; principal component analysis; sparse coding; denoising;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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