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Video quality enhancement using recursive deep residual learning network

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Abstract

Outdoor images and videos suffer from several problems, such as the hazing problem due to the particles of dust, smoke, and other particles in the atmosphere. Videos in such atmospheric conditions are subject to visible quality degradations, such as low contrast and information loss. This paper presents a dehazing algorithm that enhances the video contrast, removes haze from hazy frames, and reduces frame degradation. We use a recursive deep residual learning (DRL) network as a dehazing tool to enhance video quality. The DRL network estimates the nonlinear mapping from the space of hazy input frames to that of output dehazed frames without estimating the transmission map and the atmospheric light as in traditional dehazing methods. After that, the dehazed frame is fed back to the input of the DRL network. This process is counted as an iteration. Our proposed algorithm depends on pre-processing of frames before the dehazing process to remove noise or enhance the visual quality, because all frames contain some noise due to sensor measurement errors. Noise can be amplified in the haze removal process if ignored. We use different types of enhancement techniques before the dehazing process. In addition, we modify the DRL network to be suitable for both near infrared (NIR) and visible frames. The number of iterations in the DRL network is increased from three iterations to nine iterations and the effect of increasing the number of iterations on the output dehazed frames is studied. We stopped at nine iterations, because elapsed time increases with the increase in the number of iterations. The peak signal-to-noise ratio and correlation after the dehazing process between dehazed and input hazy frames are used as evaluation metrics for our proposed algorithm. Results show that by increasing the number of iterations in the DRL network, dehazed frames record the best contrast, the highest spectral entropy, and the highest visual quality.

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Authors and Affiliations

  1. Department of Electronics and Electrical Communication Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Abeer Ayoub, Walid El-Shafai, Eman A. Sultan, O. Zahran, Fathi E. Abd El-Samie & El-Sayed M. EL-Rabaie

  2. Electrical Department, Faculty of Technology and Education, Suez University, Suez, 43527, Egypt

    Ensherah A. Naeem

  3. Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh, 11586, Saudi Arabia

    Walid El-Shafai

  4. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia

    Fathi E. Abd El-Samie

Authors
  1. Abeer Ayoub
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  2. Ensherah A. Naeem
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  3. Walid El-Shafai
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  7. El-Sayed M. EL-Rabaie
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Correspondence to Abeer Ayoub.

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Ayoub, A., Naeem, E.A., El-Shafai, W. et al. Video quality enhancement using recursive deep residual learning network. SIViP 17, 257–265 (2023). https://doi.org/10.1007/s11760-022-02228-w

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  • DOI: https://doi.org/10.1007/s11760-022-02228-w

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