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Learning a Patch Quality Comparator for Single Image Dehazing.

Sanchayan Santra, Ranjan Mondal, Bhabatosh Chanda

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 12, 2018
    PubMed
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    This study introduces a novel image dehazing method using a Convolutional Neural Network (CNN) to select the best dehazed image patch. The approach effectively restores visibility and contrast in hazy conditions, achieving results comparable to state-of-the-art techniques.

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    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Atmospheric particles scatter light, reducing visibility and contrast in images captured during foggy or hazy conditions.
    • Hazy images often appear colorless, necessitating image dehazing techniques for restoration.
    • Existing image dehazing methods aim to recover clear, haze-free portrayals from degraded imagery.

    Purpose of the Study:

    • To propose a novel image dehazing method that effectively restores clarity and color to hazy images.
    • To develop a robust patch comparison mechanism for selecting the optimal dehazed image segment.
    • To evaluate the proposed method's performance against established state-of-the-art techniques.

    Main Methods:

    • A Convolutional Neural Network (CNN) based patch quality comparator was developed for evaluating dehazed image patches.

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  • A binary search algorithm is employed to efficiently select the best dehazed patch by comparing outputs with the original hazy image.
  • The method iteratively refines the dehazed image by selecting optimal patches.

    • Main Results:

    • The proposed method demonstrates significant improvements in image visibility and contrast restoration.
    • Quantitative and qualitative evaluations indicate good performance across various hazy conditions.
    • The method's results are found to be comparable, on average, to current state-of-the-art image dehazing approaches.

    Conclusions:

    • The developed CNN-based comparator and binary search strategy provide an effective approach to image dehazing.
    • The method successfully recovers haze-free images, enhancing visual quality.
    • The proposed technique offers a competitive alternative to existing image dehazing solutions.