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Image dehazing by artificial multiple-exposure image fusion.

Galdran, A., 2018. Image dehazing by artificial multiple-exposure image fusion. Signal Processing, 149, 135 - 147.

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Abstract

Bad weather conditions can reduce visibility on images acquired outdoors, decreasing their visual quality. The image processing task concerned with the mitigation of this effect is known as image dehazing. In this paper we present a new image dehazing technique that can remove the visual degradation due to haze without relying on the inversion of a physical model of haze formation, but respecting its main underlying assumptions. Hence, the proposed technique avoids the need of estimating depth in the scene, as well as costly depth map refinement processes. To achieve this goal, the original hazy image is first artificially under-exposed by means of a sequence of gamma-correction operations. The resulting set of multiplyexposed images is merged into a haze-free result through a multi-scale Laplacian blending scheme. A detailed experimental evaluation is presented in terms of both qualitative and quantitative analysis. The obtained results indicate that the fusion of artificially under-exposed images can effectively remove the effect of haze, even in challenging situations where other current image dehazing techniques fail to produce good-quality results. An implementation of the technique is open-sourced for reproducibility

Item Type:Article
ISSN:0165-1684
Uncontrolled Keywords:Image Dehazing; Fog Removal; Multi-Exposure Image Fusion; Gamma Correction; Image Fusion; Laplacian Pyramid
Group:Faculty of Science & Technology
ID Code:34885
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:25 Nov 2020 12:13
Last Modified:25 Nov 2020 12:13

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