[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.9708/jksci.2020.25.03.051

Noise-tolerant Image Restoration with Similarity-learned Fuzzy Association Memory

Park, Choong Shik (Dept. of Smart IT, U1 University)

Publication Information

Journal of the Korea Society of Computer and Information / v.25, no.3, 2020 , pp. 51-55 More about this Journal

Abstract

In this paper, an improved FAM is proposed by adopting similarity learning in the existing FAM (Fuzzy Associative Memory) used in image restoration. Image restoration refers to the recovery of the latent clean image from its noise-corrupted version. In serious application like face recognition, this process should be noise-tolerant, robust, fast, and scalable. The existing FAM is a simple single layered neural network that can be applied to this domain with its robust fuzzy control but has low capacity problem in real world applications. That similarity measure is implied to the connection strength of the FAM structure to minimize the root mean square error between the recovered and the original image. The efficacy of the proposed algorithm is verified with significant low error magnitude from random noise in our experiment.

Keywords

Image Restoration; Fuzzy Associative Memory; Similarity Learning; Similarity Measure;

Citations & Related Records

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