Image Restoration Filter for Preserving High Frequency Components in Impulse Noise Environments

임펄스 잡음 환경에서 고주파 성분을 보존하기 위한 영상 복원 필터

  • Cheon, Bong-Won (Dept. of Control and Instrumentation Eng., Pukyong National University) ;
  • Kim, Nam-Ho (Dept. of Control and Instrumentation Eng., Pukyong National University)
  • Received : 2019.01.29
  • Accepted : 2019.02.26
  • Published : 2019.04.30


Noise removal is one of the required step in processing digital video and there are many researches to develop algorithm that fits with its purpose and environment. However, present impulse noise removal methods are lacking in its function in terms of removing noise in edge and high frequency factors. Therefore, this research has Extended range of masks depending on density to determine noise so that high frequency factors can be preserved. The range of resolution is set based on median and standard deviation of inside resolution after removing impulse noise. afterwards, those resolution within the range are calculated by adding weight to have the final output value. The suggested algorithm has an enhanced function in removing noise in various areas with many edge and high frequency factors than present methods and their functions are compared through simulation.

잡음 제거는 디지털 영상처리 과정에서 필수적으로 이루어지며, 다양한 분야에서 그 목적과 환경에 맞는 알고리즘을 개발하기 위해 많은 연구가 진행되고 있다. 그러나 기존 임펄스 잡음 제거 방법들은 영상의 에지 성분 및 고주파 성분의 잡음 제거에 다소 미흡한 성능을 보이고 있다. 따라서 본 논문에서는 고주파 성분을 보존하기 위해 잡음 판단에 따른 잡음 밀도에 따라 마스크의 범위를 확장하였다. 선택된 마스크는 임펄스 잡음을 제외한 내부 화소의 메디안 값과 표준편차를 기준으로 화소 범위가 설정된다. 그리고 화소 범위에 존재하는 화소는 거리에 따른 가중치를 적용하여 최종 출력 계산에 사용하였다. 제안한 알고리즘은 기존 방법에 비해 영상의 에지 부분 및 고주파 성분이 많은 영역에서 잡음 제거성능이 우수하였으며, 시뮬레이션을 통해 성능을 비교하였다.


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Fig. 1 Filtering mask

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Fig. 2 Test image (a) Lena (b) Enlarged Noise image (Lena) (c) Barbara (d) Enlarged Noise image (Barbara) (e) Baboon (f) Enlarged Noise image (Baboon)

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Fig. 3 Enlarged image of simulation result

Table. 1 PSNR comparison for each filter(Lena)

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Table. 2 PSNR comparison for each filter(Barbara)

HOJBC0_2019_v23n4_394_t0002.png 이미지

Table. 3 PSNR comparison for each filter(Baboon)

HOJBC0_2019_v23n4_394_t0003.png 이미지


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