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- W4380049033 abstract "디지털 영상 기술의 발전으로 인해 다양한 분야에서 영상 처리 기술을 연구, 개발하고 있다. 하지만, 필연적으로 주변 환경의 영향을 받는 시각적 모니터링 장비 혹은 CMOS 센서, CT, MRI, SEM 등과 같은 특수한 장치는 잡음이 영상의 화질에 큰 영향을 미칠 수 있다. 영상에 발생하는 AWGN은 모든 주파수 대역에서 나타나는 잡음으로, 첨가된 잡음 함수가 가우시안 분포를 따르는 것이 특징이다. AWGN을 제거하기 위해 과거부터 많은 연구가 제안되었으나, 심각한 블러링 현상으로 인해 화질 개선 및 에지 보존 성능이 미흡했다. 따라서 본 논문은 훼손된 영상을 효과적으로 복원하기 위해 공간 및 적응적 가중치를 이용한 알고리즘을 제안하였다. 제안한 필터는 잡음 밀도 가중치와 중앙값 가중치를 공간 가중치에 사용하여 훼손이 심한 주변 화소를 제거하였으며, 중심 화소에 적응적 가중치 필터를 실행하여 잡음 영상을 세밀하게 복원하였다. With advances in digital image technology, researchers of various fields have developed image processing technologies. However, noise can affect image quality significantly in visual monitoring equipment or special devices -such as CMOS sensors, CT, MRI, and SEM-that are inevitably affected by the surrounding environment. AWGN generated in images is a noise that appears in all frequency bands and is characterized by the additive noise function that follows a Gaussian distribution. Many studies have been proposed in the past to remove AWGN. However, they could not improve image quality sufficiently and had poor edge preservation performance due to severe blurring. Therefore, this study proposes an algorithm that uses spatial and adaptive weights to restore damaged images effectively. The proposed filter uses a noise density weight and a median weight as spatial weights to remove surrounding pixels that have been severely damaged. In addition, it performs adaptive weight filtering on the center pixel to restore noisy images in detail." @default.
- W4380049033 created "2023-06-10" @default.
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- W4380049033 date "2023-05-31" @default.
- W4380049033 modified "2023-10-17" @default.
- W4380049033 title "AWGN Removal Algorithm using Spatial and Adaptive Weights" @default.
- W4380049033 doi "https://doi.org/10.6109/jkiice.2023.27.5.628" @default.
- W4380049033 hasPublicationYear "2023" @default.
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