[논문]복합 영상 잡음 환경에서 변형된 퍼지가중치 및 결합가중치를 사용한 디지털 스위칭 필터 알고리즘

천봉원; 김남호

doi:10.6109/jkiice.2021.25.5.645

복합 영상 잡음 환경에서 변형된 퍼지가중치 및 결합가중치를 사용한 디지털 스위칭 필터 알고리즘
Digital Switching Filter Algorithm using Modified Fuzzy Weights and Combined Weights in Mixed Image Noise Environment 원문보기

한국정보통신학회논문지 = Journal of the Korea Institute of Information and Communication Engineering, v.25 no.5, 2021년, pp.645 - 651

천봉원 (Dept. of Smart Robot Convergence and Application Eng., Pukyong National University) , 김남호 (Dept. of Control and Instrumentation Eng., Pukyong National University)

초록
AI-Helper

현대 사회는 4차 산업혁명의 영향에 의해 다양한 디지털 통신 장비가 사용되고 있다. 이에 따라 데이터 전송 과정에서 발생하는 잡음제거에 관심이 높아지고 있으며, 효율적으로 영상을 복원하기 위한 연구가 진행되고 있다. 하지만 복합적인 잡음에 훼손된 영상을 복원하는데 어려움을 겪고 있으며, 잡음의 특징에 따라 효과적으로 영상을 복원하는 디지털 필터가 요구되고 있다. 본 논문에서는 디지털 영상 전송 과정에서 발생하는 복합잡음을 제거하기 위한 디지털 스위칭 필터 알고리즘을 제안한다. 제안한 알고리즘은 잡음판단을 통해 필터링 과정을 스위칭하며 마스크 내부의 화소값들을 기준으로 퍼지가중치 및 결합가중치를 사용하여 영상을 복원한다. 제안한 알고리즘을 평가하기 위해 기존 필터 알고리즘들과 시뮬레이션을 통하여 비교하였다. 시각적인 평가를 위해 필터링 결과를 확대하여 비교하였으며, 정량적인 평가를 위해 PSNR 비교를 사용하여 분석하였다.

Abstract ▼ AI-Helper

With the advent of the Fourth Industrial Revolution, modern society uses a diverse pool of devices. In this context, there is increasing interest in removing various kinds of noise arising in data transmission. However, it is difficult to restore image that damaged by mixed noise, and a digital filter that effectively restores an image according to the characteristics of the noise is required. In this paper, we propose a digital switching filter algorithm to remove mixed noise generated during digital image transmission. The proposed algorithm switches the filtering process through noise judgment and reconstructs the image using fuzzy weights and combined weights based on the pixel values inside the mask. To evaluate the proposed algorithm, we compared it with existing filter algorithms through simulation. Filtering results were expanded and compared for visual evaluation, and PSNR comparison was used for quantitative evaluation.

주제어

표/그림 (8)

그림 Fig. 1 Modified fuzzy membership function
그림 Fig. 2 Flowchat of proposed algorithm
그림 Fig. 3 Simulation result (Boat image) (a) Original image (b) Noisy image(σ = 15, P = 30 %) (c) CWMF (d) ASMF (e) FLBF (f) PFA
그림 Fig. 4 Simulation result (Barbara image) (a) Original image (b) Noisy image(σ = 15, P = 30%) (c) CWMF (d) ASMF (e) FLBF (f) PFA
표 Table. 1 PSNR comparison of Boat image (σ = 15)
표 Table. 2 PSNR comparison of Barbara image (σ = 15)
표 Table. 3 SSIM comparison of Boat image (σ = 15)
표 Table. 4 SSIM comparison of Barbara image (σ = 15)

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