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Appl. Sci. 2018, 8(8), 1321; https://doi.org/10.3390/app8081321

Image Dehazing and Enhancement Using Principal Component Analysis and Modified Haze Features

Department of Image, Graduate School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University, Seoul 06974, Korea
These authors contributed equally to this work.
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Received: 5 June 2018 / Revised: 17 July 2018 / Accepted: 24 July 2018 / Published: 8 August 2018
(This article belongs to the Special Issue Advanced Intelligent Imaging Technology)
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Abstract

This paper presents a computationally efficient haze removal and image enhancement methods. The major contribution of the proposed research is two-fold: (i) an accurate atmospheric light estimation using principal component analysis, and (ii) learning-based transmission estimation. To reduce the computational cost, we impose a constraint on the candidate pixels to estimate the haze components in the sub-image. In addition, the proposed method extracts modified haze-relevant features to estimate an accurate transmission using random forest. Experimental results show that the proposed method can provide high-quality results with a significantly reduced computational load compared with existing methods. In addition, we demonstrate that the proposed method can significantly enhance the contrast of low-light images according to the assumption on the visual similarity between the inverted low-light and haze images. View Full-Text
Keywords: image enhancement; dehazing; random forest; principal component analysis; low-light image enhancement; supervised learning image enhancement; dehazing; random forest; principal component analysis; low-light image enhancement; supervised learning
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Kim, M.; Yu, S.; Park, S.; Lee, S.; Paik, J. Image Dehazing and Enhancement Using Principal Component Analysis and Modified Haze Features. Appl. Sci. 2018, 8, 1321.

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