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Deep Illumination-Aware Dehazing With Low-Light and Detail Enhancement | IEEE Journals & Magazine | IEEE Xplore

Deep Illumination-Aware Dehazing With Low-Light and Detail Enhancement


Abstract:

We present a novel dehazing framework for real-world images that contain both hazy and low-light areas. Dehazing and low-light enhancements are unified by using an illumi...Show More

Abstract:

We present a novel dehazing framework for real-world images that contain both hazy and low-light areas. Dehazing and low-light enhancements are unified by using an illumination map that is estimated using a proposed convolutional neural network. The illumination map is then used as a component for three different tasks: atmospheric light estimation, transmission map estimation, and low-light enhancement, thereby enabling the solving of interrelated low-level vision problems simultaneously. To train the neural network to perform both dehazing and low-light enhancement, we synthesize hazy and low-light images from normal images. Experimental results demonstrate that the proposed method quantitatively and qualitatively outperforms state-of-the-art algorithms in real-world image dehazing.
Published in: IEEE Transactions on Intelligent Transportation Systems ( Volume: 23, Issue: 3, March 2022)
Page(s): 2494 - 2508
Date of Publication: 12 October 2021

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