Name of the Speaker: Praveen Kandula (EE17D026)
Guide: Dr. Rajagopalan A. N
Venue/Online meeting link: https://meet.google.com/sss-nqdq-nee
Date/Time: 29th September 2022, 3.00 pm
Satellite images are typically subject to multiple photometric distortions. Different factors affect the quality of satellite images, including changes in atmosphere, surface reflectance, sun illumination, viewing geometries etc., resulting in multiple photometric distortions in the satellite images. In supervised networks, the availability of paired datasets is a strong assumption. Consequently, many unsupervised algorithms have been proposed to address this problem. These methods synthetically generate a large dataset of degraded images using image formation models. A neural network is then trained with an adversarial loss to discriminate between images from distorted and clean domains. However, these methods yield suboptimal performance when tested on real images that do not necessarily conform to the generation mechanism. Also, they require a large amount of training data and are rendered unsuitable when only few images are available. To address these important issues, we propose a distortion disentanglement and knowledge distillation framework for satellite image restoration. Our algorithm requires only two images: the distorted satellite image to be restored and a reference image with similar semantics. Ablation studies show that our proposed mechanism successfully disentangles distortion. Exhaustive experiments on different timestamps of Google-Earth images and on publicly available datasets, LEVIR-CD and SZTAKI, show that our proposed mechanism can tackle a variety of distortions and outperforms existing state-of-the-art restoration methods visually as well as on quantitative metrics.