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Homogeneous and Non-homogeneous Image Dehazing Using Deep Neural Network

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Computer Vision and Image Processing (CVIP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1567))

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Abstract

Haze poses challenges in many vision-related applications. Thus, dehazing an image becomes popular among vision researchers. Available methods use various priors, deep learning models, or a combination of both to get plausible dehazing solutions. This paper reviews some recent advancements and their results on both homogeneous and non-homogeneous haze datasets. Intending to achieve haze removal for both types of haze, we propose a new architecture, developed on a convolutional neural network (CNN). The network is developed based on reformulating the atmospheric scattering phenomenon and estimating haze density to extract features for both types of haze. The haze-density estimation is supplemented by channel attention and pixel attention modules. The model is trained on perceptual loss. The quantitative and qualitative results demonstrate the efficacy of our approach on homogeneous as well as non-homogeneous haze as compared to the existing methods, developed for a particular type.

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Authors and Affiliations

  1. DA-IICT, Gandhinagar, Gujarat, India

    Manan Gajjar & Srimanta Mandal

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  1. Manan Gajjar
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  2. Srimanta Mandal
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Correspondence to Srimanta Mandal .

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Editors and Affiliations

  1. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  2. Indian Institute of Technology Ropar, Ropar, India

    Subrahmanyam Murala

  3. Jadavpur University, Kolkata, India

    Ananda Chowdhury

  4. Indian Institute of Technology Ropar, Ropar, India

    Abhinav Dhall

  5. Indian Institute of Technology Ropar, Ropar, India

    Puneet Goyal

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Gajjar, M., Mandal, S. (2022). Homogeneous and Non-homogeneous Image Dehazing Using Deep Neural Network. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1567. Springer, Cham. https://doi.org/10.1007/978-3-031-11346-8_33

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  • DOI: https://doi.org/10.1007/978-3-031-11346-8_33

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