Dehazing using non-local regularization with iso-depth neighbor-fields = 동일 깊이 근접장과 비국소 정규화를 이용한 헤이즈 제거 알고리즘

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Removing haze from a single image is a severely ill-posed problem due to the lack of scene information. General dehazing algorithms estimate airlight initially using natural image statistics and then propagate the incompletely estimated airlight to build a dense transmission map, yielding a haze-free image. Propagating haze is different from other regularization problems, as haze is strongly correlated with depth according to the physics of light transport in participating media. However, since there is no depth information available in single-image dehazing, traditional regularization methods with a common grid random field often suffer from haze isolation artifacts caused by abrupt changes in scene depths. In this paper, to overcome the haze isolation problem, we propose a non-local regularization method by combining Markov random fields (MRFs) with nearest-neighbor fields (NNFs), based on our insightful observation that the NNFs searched in a hazy image associate patches at the similar depth, as local haze in the atmosphere is proportional to its depth. We validate that the proposed method can regularize haze effectively to restore a variety of natural landscape images. This proposed regularization method can be used separately with any other dehazing algorithms to enhance haze regularization.
Advisors
Kim, Min Hyukresearcher김민혁researcher
Description
한국과학기술원 :전산학부,
Publisher
한국과학기술원
Issue Date
2017
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전산학부, 2017.2,[iv, 38 p. :]

Keywords

dehazing; non-local regularization; image restoration; image processing; propagation; 헤이즈 제거; 비국소 정규화; 이미지 복원; 영상 처리; 전파

URI
http://hdl.handle.net/10203/243406
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=675487&flag=dissertation
Appears in Collection
CS-Theses_Master(석사논문)
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