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Video and image quality improvement using an enhanced optimized dehazing technique

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Abstract

The detrimental effects of atmospheric haze frequently plague outdoor imagery. This phenomenon arises from the scattering of light by minute particles within the ambient environment surrounding the scene to be imaged. Haze engenders an overall whitening of the image, leading to diminished contrast. To address these issues and enhance the quality of hazy images and videos, an enhanced dehazing technique is proposed. The proposed technique includes image enhancement before the optimal dehazing process. The enhancement stage entails the implementation of both homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE), serving to control the image dynamic range, while concurrently heightening the image contrast. The culminating stage encompasses an optimized dehazing technique, adept at expunging haze-induced artifacts from images. The homomorphic processing and CLAHE, applied in the pre-processing step, establish a foundation for the subsequent dehazing procedure. This proposed methodology is systematically applied to a gamut of visual outputs, including visible videos, Near-Infrared (NIR) frames, and authentic hazy images. Comparative evaluations of the proposed technique, homomorphic-processing-based enhanced dehazing, and standalone dehazing techniques is undertaken on different video types encompassing five frames. It is evident that the proposed technique, synergizing the homomorphic processing, CLAHE, and dehazing, outperforms alternative strategies. Furthermore, the proposed technique is subjected to a comparative study with various existing dehazing techniques over real hazy images. The assessment depends on Peak Signal-to-Noise Ratio (PSNR), correlation, and entropy metrics. The results underscore the efficacy of the proposed technique, particularly in terms of spectral entropy enhancement of dehazed frames. For both visible and NIR frames, the percentages of enhancement by the proposed technique are 17.66% and 118.48%, respectively.

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Acknowledgements

The authors are very grateful to all the institutions in the affiliation list for successfully performing this research work. The authors would like to thank Prince Sultan University for their support.

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The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt

    Abeer Ayoub & Ehab K. I. Hamad

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

    Walid El-Shafai, Fathi E. Abd El-Samie & El-Sayed M. EL-Rabaie

  3. Security Engineering Lab, Computer Science Department, Prince Sultan University, 11586, Riyadh, 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, 11671, Riyadh, Saudi Arabia

    Fathi E. Abd El-Samie

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  1. Abeer Ayoub
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  2. Walid El-Shafai
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  4. Ehab K. I. Hamad
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Ayoub, A., El-Shafai, W., El-Samie, F.E.A. et al. Video and image quality improvement using an enhanced optimized dehazing technique. Multimed Tools Appl 84, 22681–22699 (2025). https://doi.org/10.1007/s11042-024-19263-z

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