An Adaptive Combination of Dark and Bright Channel Priors for Single Image Dehazing

Vincent Whannou de Dravo, Jessica El Khoury, Jean Baptiste Thomas, Alamin Mansouri, Jon Yngve Hardeberg
<span title="2017-09-11">2017</span> <i title="Society for Imaging Science &amp; Technology"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/emray5vynfh25mrotrlaax2pkm" style="color: black;">Final program and proceedings (Color and Imaging Conference)</a> </i> &nbsp;
Dehazing methods based on prior assumptions derived from statistical image properties fail when these properties do not hold. This is most likely to happen when the scene contains large bright areas, such as snow and sky, due to the ambiguity between the airlight and the depth information. This is the case for the popular dehazing method Dark Channel Prior. In order to improve its performance, the authors propose to combine it with the recent multiscale STRESS, which serves to estimate Bright
more &raquo; ... annel Prior. Visual and quantitative evaluations show that this method outperforms Dark Channel Prior and competes with the most robust dehazing methods, since it separates bright and dark areas and therefore reduces the color cast in very bright regions.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2352/j.imagingsci.technol.2017.61.4.040408">doi:10.2352/j.imagingsci.technol.2017.61.4.040408</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/webo55n5cvhdvdx5kkk7zabeki">fatcat:webo55n5cvhdvdx5kkk7zabeki</a> </span>
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