Zero shot framework for satellite image restoration
Praveen Kandula A. N. Rajagopalan
Satellite images are typically subject to multiple distortions. Different
factors affect the quality of satellite images, including changes in
atmosphere, surface reflectance, sun illumination, viewing geometries etc.,
limiting its application to downstream tasks. 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 a few images are
available. We propose a distortion disentanglement and knowledge distillation
framework for satellite image restoration to address these important issues.
Our algorithm requires only two images: the distorted satellite image to be
restored and a reference image with similar semantics. Specifically, we first
propose a mechanism to disentangle distortion. This enables us to generate
images with varying degrees of distortion using the disentangled distortion and
the reference image. We then propose the use of knowledge distillation to train
a restoration network using the generated image pairs. As a final step, the
distorted image is passed through the restoration network to get the final
output. Ablation studies show that our proposed mechanism successfully
disentangles distortion.