Zero-Shot Single Image Restoration Through Controlled Perturbation of Koschmieder's Model

Real-world image degradation due to light scattering can be described based on the Koschmieder's model. Training deep models to restore such degraded images is challenging as real-world paired data is scarcely available and synthetic paired data may suffer from domain-shift issues. In this paper, a zero-shot single real-world image restoration model is proposed leveraging a theoretically deduced property of degradation through the Koschmieder's model. Our zero-shot network estimates the parameters of the Koschmieder's model, which describes the degradation in the input image, to perform image restoration. We show that a suitable degradation of the input image amounts to a controlled perturbation of the Koschmieder's model that describes the image's formation. The optimization of the zeroshot network is achieved by seeking to maintain the relation between its estimates of Koschmieder's model parameters before and after the controlled perturbation, along with the use of a few no-reference losses. Image dehazing and underwater image restoration are carried out using the proposed zero-shot framework, which in general outperforms the state-of-the-art quantitatively and subjectively on multiple standard real-world image datasets. Additionally, the application of our zero-shot framework for low-light image enhancement is also demonstrated.