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Single Image Dehazing Using Deep Belief Neural Networks to Reduce Computational Complexity

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • J. Samuel Manoharan

    (Bharathiyar College of Engg. and Technology, Department of ECE)

  • G. Jayaseelan

    (Bharathiyar College of Engg. and Technology, Department of ECE)

Abstract

Image dehazing is one of the classification of image restoration methods in image processing and is gaining wide spread attention in recent times to dehaze the image/video in real time. They find an ominous utility in transportation systems and is of great aid to commuters in highly hazy urban as well as hilly terrains. They are also being widely researched to develop real time dehazing methods in aircraft to provide uninterrupted view through haze and mist. Dark channel prior techniques have been widely used in image dehazing with more research being done to improve the quality of image as well as to reduce the computation time. This paper proposes a computation complexity reduction mechanism in dehazing by utilizing the convolution properties of deep belief neural networks to train the data sets in the least possible time with improved image quality.

Suggested Citation

  • J. Samuel Manoharan & G. Jayaseelan, 2020. "Single Image Dehazing Using Deep Belief Neural Networks to Reduce Computational Complexity," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 1471-1478, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_151
    DOI: 10.1007/978-3-030-41862-5_151
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