Rain Streaks Removal in Images using Extended Generative Adversarial-based Deraining Framework

Abstract

The visual quality of photographs and videos can be negatively impacted by various weather conditions, such as snow, haze, or rain, affecting the quality of the images and videos. Such impacts may greatly affect outdoor vision systems that rely on image/video data. It has recently drawn a lot of interest to remove rain streaks from a single image. Several deep learning-based methods have been introduced to address the issue of removing rain streaks from a single image. Still, the efficiency of rain streak removal with enhanced quality is challenging. Hence, a novel deep-learning method is introduced for rain streak removal. The proposed Extended Generative Adversarial based De-raining (Ex_GADerain) is the enhanced version of a traditional Generative adversarial network (GAN). The proposed Ex_GADerain introduced a Self-Attention based Convolutional Capsule Bidirectional Network (SA-CCapBiNet) based generator for enhancing the rain streaks removal process. Also, the loss function estimation using the adversarial loss and the mean absolute error loss minimizes the information loss during training. The minimal information loss enhances the generalization capability of Ex_GADerain, and hence the enhanced performance is acquired. The quality assessment of a derained image based on various assessment measures like SSIM, PSNR, RMSE, and DSSIM improved performance compared to the conventional rain streak removal methods. The maximal SSIM and PSNR acquired by the Ex_GADerain are 0.9923 and 26.7052, respectively. The minimal RMSE and DSSIM acquired by the Ex_GADerain are 0.9367 and 0.0051, respectively.