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STRASS Dehazing: Spatio-Temporal Retinex-Inspired Dehazing by an Averaging of Stochastic Samples

Zhe Yu1, Bangyong Sun1,3,*, Di Liu2, Vincent Whannou de Dravo1, Margarita Khokhlova4, Siyuan Wu3

1 School of Printing Packaging Engineering and Digital Media, Xi’an University of Technology, Xi’an, 710048, China
2 Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China
3 Key Laboratory of Spectral Imaging Technology CAS, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, 710119, China
4 LASTIG, Université Gustave Eiffel, Écully, 69134, France

* Corresponding Author: Bangyong Sun. Email:

(This article belongs to this Special Issue: Green, Recycled and Intelligent Technologies in Printing and Packaging)

Journal of Renewable Materials 2022, 10(5), 1381-1395.


In this paper, we propose a neoteric and high-efficiency single image dehazing algorithm via contrast enhancement which is called STRASS (Spatio-Temporal Retinex-Inspired by an Averaging of Stochastic Samples) dehazing, it is realized by constructing an efficient high-pass filter to process haze images and taking the influence of human vision system into account in image dehazing principles. The novel high-pass filter works by getting each pixel using RSR and computes the average of the samples. Then the low-pass filter resulting from the minimum envelope in STRESS framework has been replaced by the average of the samples. The final dehazed image is yielded after iterations of the high-pass filter. STRASS can be run directly without any machine learning. Extensive experimental results on datasets prove that STRASS surpass the state-of-the-arts. Image dehazing can be applied in the field of printing and packaging, our method is of great significance for image pre-processing before printing.


Cite This Article

Yu, Z., Sun, B., Liu, D., Whannou, V., Khokhlova, M. et al. (2022). STRASS Dehazing: Spatio-Temporal Retinex-Inspired Dehazing by an Averaging of Stochastic Samples. Journal of Renewable Materials, 10(5), 1381–1395.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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