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Improvement of Non-Local Means Algorithm Using Similarity in Image

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(11), pp.145-152
  • DOI : 10.9708/jksci.2024.29.11.145
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 29, 2024
  • Accepted : November 21, 2024
  • Published : November 29, 2024

Jeongwoo Lee 1 Heeyeon Jo 1 Jiyun Byun 2 Hongrae Lee 1

1연세대학교
2연세대학교 미래캠퍼스 소프트웨어학부

Accredited

ABSTRACT

With the widespread adoption of smartphones, acquiring images has become easier. However, challenges arise due to uneven lighting conditions at night and the degradation and noise introduced during image transmission and compression. To minimize this noise and improve image quality, Non-Local Means (NLM) techniques are used, which unlike traditional methods, seek out patches within the image that are similar to the current patch to eliminate noise. However, a drawback of NLM is the diminishing utility as the similar patches become larger. This paper proposes a noise reduction method that utilizes the Sum of Absolute Differences to calculate similarity and applies weights accordingly. The proposed algorithm demonstrates an average improvement of 6.911dB in Peak Signal-to-Noise Ratio (PSNR) on Salt and Pepper noise images, showing a 0.713dB improvement over traditional NLM. When the proposed algorithm is applied to existing NLM optimization papers, performance improvements can be expected.

Citation status

* References for papers published after 2023 are currently being built.

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