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Image Defogging Algorithm Based on Sparse Representation

Author

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  • Di Fan
  • Xinyun Guo
  • Xiao Lu
  • Xiaoxin Liu
  • Bo Sun

Abstract

Aiming at the problems of low contrast and low definition of fog degraded image, this paper proposes an image defogging algorithm based on sparse representation. Firstly, the algorithm transforms image from RGB space to HSI space and uses two-level wavelet transform extract features of image brightness components. Then, it uses the K-SVD algorithm training dictionary and learns the sparse features of the fog-free image to reconstructed I-components of the fog image. Using the nonlinear stretching approach for saturation component improves the brightness of the image. Finally, convert from HSI space to RGB color space to get the defog image. Experimental results show that the algorithm can effectively improve the contrast and visual effect of the image. Compared with several common defog algorithms, the percentage of image saturation pixels is better than the comparison algorithm.

Suggested Citation

  • Di Fan & Xinyun Guo & Xiao Lu & Xiaoxin Liu & Bo Sun, 2020. "Image Defogging Algorithm Based on Sparse Representation," Complexity, Hindawi, vol. 2020, pages 1-8, July.
    • Handle: RePEc:hin:complx:6835367
    DOI: 10.1155/2020/6835367
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    Cited by:

    1. Fangsheng Wu & Changan Zhu & Jinxiu Xu & Mohammed Wasim Bhatt & Ashutosh Sharma, 2022. "Research on image text recognition based on canny edge detection algorithm and k-means algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 72-80, March.

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