Dr. Arijit Sur

Outdoor images or videos are often deteriorated due to the presence of atmospheric noise. The characteristics of such noise span from periodic or pseudo-periodic to exponentially varying with the pixel's depth. For example, adverse weather conditions, such as rainy and haziness in the images or videos. While the rain-streaks depicts the pseudo-periodic nature, the haziness varies exponentially with the depth of the pixel. These degradations may cause problems in real-time applications such as Surveillance, SLAM related autonomous vehicle motion, UAV & Satellite Imaging (Satellite Optical Images), and the Missile communication system (Target Object Detection). The proposed work aims to restore the degraded images or videos and can be applied to any of the tasks mentioned above. With the evolution of Deep Learning, several methods have been proposed for such type of image/video restoration tasks. However, the availability of realistic data and adapting to the realistic noise is still a major challenge to address. Besides this, the restored images suffer from severe visual artifacts such as color degradation and halo artifacts.