LAUSR

Poor weather conditions are detrimental to transportation systems and increase the likelihood of road accidents. Haze and fog are the most responsible atmospheric parameters affecting visibility and hence the traffic performance. The intelligent driving assistance systems developed for automatic vehicles use clear vision for various smart applications like keeping within the correct lane and recognize traffic signs. Bad weather decreases the visibility significantly based on the intensity of fog and haze. So, there is a need for the restoration of clear visibility. This paper introduces a novel visibility restoration approach based on thresholding and gamma transformation method. A step-by-step process for the proposed method is as follows. First, the proper selection of an atmospheric light value is responsible for the validation of the color and contrast of the recovered images. The threshold process makes it possible to estimate atmospheric light. Then, accurately estimating the transmission depth from object to object in small time is the most challenging aspect due to unequal distribution. To solve the problem, the gamma transformation method is used to correctly estimate the depth. Finally, restore the scene radiance from the hazy and foggy images. The experimental results show that the proposed measurement ensures good uniformity about qualitative and quantitative evaluation using eight performance metrics: contrast gain, percentage of saturated pixels, blind contrast assessment, structural similarity index measure (SSIM), image visibility measurement (IVM), mean square error (MSE), visual contrast measure (VCM), and peak signal-to-noise ratio (PSNR). The proposed algorithm excels in previous works in terms of processing time and visibility restoration results comparable to sophisticated state-of-the-art techniques.