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Tight lower bound on transmission for single image dehazing

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Abstract

Effective functioning of outdoor vision systems depends upon the quality of input. Varying effects of light create different weather conditions (like raining, snowfall, haze, mist, fog, and cloud) due to optical properties of light and physical existence of different size particles in the atmosphere. Thus, outdoor images and videos captured in adverse environmental conditions have poor visibility due to scattering of light by atmospheric particles. Visibility restoration (dehazing) of degraded (hazy) images is critical for the useful performance of outdoor vision systems. Most of the existing methods of image dehazing considered atmospheric scattering model (ASM) to improve the visibility of hazy images or videos. According to ASM, the visual quality of dehazed image depends upon accurate estimation of transmission. Existing methods presented different priors with strong assumptions to estimate transmission. The proposed method introduces a tight lower bound on transmission. However, the accuracy of the proposed tight lower bound depends upon minimum color channel of haze-free image. Therefore, a prior is proposed to estimate the minimum color channel of the haze-free image. Furthermore, a blind assessment metric is proposed to evaluate the dehazing methods. Restored and matching corner points of the hazy and haze-free image are used to compute the proposed blind assessment metric. Obtained results are compared with renowned dehazing methods by qualitative and quantitative analysis to prove the efficacy of the proposed method.

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  1. ABV-IIITM, Gwalior, India

    Suresh Chandra Raikwar & Shashikala Tapaswi

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  1. Suresh Chandra Raikwar
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Correspondence to Suresh Chandra Raikwar.

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Authors Suresh Chandra Raikwar and Shashikala Tapaswi declare that they do not have any conflict of interest.

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Raikwar, S.C., Tapaswi, S. Tight lower bound on transmission for single image dehazing. Vis Comput 36, 191–209 (2020). https://doi.org/10.1007/s00371-018-1596-5

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