2D histogram-based contrast enhancement using an adaptive weight kernel = 적응적 가중치 커널을 이용한 2차원 히스토그램 기반 대비 강화

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Contrast enhancement is often used as preprocessing in computer vision, image processing, and pattern recognition. The contrast enhancement method using a 2D histogram is superior to the 1D histogram-based contrast enhancement because it enhances contrast using contextual information between adjacent pixels. In the 2D histogram-based algorithm, the kernel, which weights according to the difference in pixel values between adjacent pixels in the 2D histogram, is used. Since the kernel is fixed for every image, it can not provide the desired contrast adaptively to image contents. Thus, we propose an adaptive weight kernel based on the statistical information of the 2D histogram through linear regression. Once the kernel is obtained, it is used to strengthen the contrast efficiently through optimization techniques. Also, by using only the partial contextual information of the image, we drastically reduce the computational time which is a disadvantage of the conventional 2D histogram-based algorithms. Experimental results show that the proposed method is superior to conventional algorithms in terms of contrast enhancement performance.
Advisors
Kim, Changickresearcher김창익researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2017
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2017.2,[iv, 45p :]

Keywords

Contrast enhancement; 2D histogram; Adaptive weight kernel; Optimization; Window setting; 대비 강화; 2차원 히스토그램; 적응적 가중치 커널; 최적화; 윈도우 설정

URI
http://hdl.handle.net/10203/243236
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=675341&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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