TY  - EJOU
AU  - Yu, Zhe 
AU  - Sun, Bangyong 
AU  - Liu, Di 
AU  - Dravo, Vincent Whannou de 
AU  - Khokhlova, Margarita 
AU  - Wu, Siyuan 

TI  - STRASS Dehazing: Spatio-Temporal Retinex-Inspired Dehazing by an Averaging of Stochastic Samples
T2  - Journal of Renewable Materials

PY  - 2022
VL  - 10
IS  - 5
SN  - 2164-6341

AB  - 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 Then the low-pass filter resulting from the minimum envelope in STRESS framework has been replaced by the average of the samples.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.
KW  - Image dehazing; contrast enhancement; high-pass filter; image reconstruction

DO  - 10.32604/jrm.2022.018262