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

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2020, 25(3), pp.51-55
  • DOI : 10.9708/jksci.2020.25.03.051
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : February 11, 2020
  • Accepted : March 9, 2020
  • Published : March 31, 2020

Park Choong Shik 1

1U1대학교

Accredited

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.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.