A robust visibility restoration framework for rainy weather degraded images

Abstract

Visibility restoration of color rainy images is inevitable task for the researchers in many vision based applications. Rain produces a visual impact on image, so that the intensity and visibility of image is low. Therefore, there is a need to develop a robust visibility restoration algorithm for the rainy images. In this paper we proposed a robust visibility restoration framework for the images captured in rainy weather. The framework is the combined form of convolution neural network for rain removal and low light image enhancement for low contrast. The output results of the proposed framework and other latest de-rainy algorithms are estimated in terms of PSNR, SSIM and UIQI on rainy image from different databases. The quantitative and qualitative results of the proposed framework are better than other de-rainy algorithms. Finally, the obtained visualization result also shows the efficiency of the proposed framework. 2018 Narendra Singh Pal, Shyam Lal, Kshitij Shinghall; published by UIKTEN.