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Single Frame-Based Video Dehazing with Adversarial Learning

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Computer Vision and Image Processing (CVIP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1568))

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Abstract

Hazy environmental conditions degrade the quality of captured videos which leads to poor visibility and color distortion in videos. Such deterioration of captured video quality is mainly because of the attenuation caused by the scattering of light due to the haze particles present in the environment. In this paper, we propose an adversarial learning based single frame video dehazing encoder-decoder network. The proposed method comprises of Dilated Residual Block (DRB) used as encoder and Skip connection. DRB module is used to gain more contextual information by achieving large receptive fields. Skip connections are established between each encoder and decoder, which helps to detect and give more attention to haze specific features by adjusting the weights of learned feature maps automatically. This helps to extract the haze-relevant feature maps and recovers the haze-free video frame by using Channel Attention Block (CAB) and Residual Block (ResB) respectively. An extensive quantitative and qualitative analysis of the proposed method is done on benchmark synthetic hazy video database namely DAVIS-16 and NYU depth. Experimental result shows that the proposed method outperforms the other existing state-of-the-art (SOTA) approaches for video dehazing.

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Author information

Authors and Affiliations

  1. Punjab Engineering College, Chandigarh, India

    Vijay M. Galshetwar & Sachin Chaudhary

  2. Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia

    Prashant W. Patil

Authors
  1. Vijay M. Galshetwar
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  2. Prashant W. Patil
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  3. Sachin Chaudhary
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Corresponding author

Correspondence to Vijay M. Galshetwar .

Editor information

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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Galshetwar, V.M., Patil, P.W., Chaudhary, S. (2022). Single Frame-Based Video Dehazing with Adversarial Learning. 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 1568. Springer, Cham. https://doi.org/10.1007/978-3-031-11349-9_4

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  • DOI: https://doi.org/10.1007/978-3-031-11349-9_4

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