Restoration and Enhancement of Aerial and Synthetic Aperture Radar Images Using Generative Deep Image Prior Architecture

Restoration and enhancement of low light images is an inevitable pre-processing activity among remote sensing, aerial and satellite imaging modalities. The images captured under various atmospheric conditions are distorted. Therefore, they need a thorough conditioning before being analysed. In this paper, we propose a retinex-based variational framework designed under a generative deep image prior architecture to restore and enhance distorted images from satellite, aerial and remote sensing applications. The model handles data-correlated speckle noise found in active image sensing modalities, duly considering its distribution. The data-fidelity aspect of the proposed variational framework is designed using the Bayesian Maximum A Posteriori (MAP) estimate, assuming that the input images are contaminated with Gamma distributed speckled interference. Further, model is catered to handle various noise distributions, such as Gaussian and Poisson, by appropriately altering the data fidelity term specific to the distribution, without modifying the architecture of the model. The variational retinex model employed herein also addresses contrast degradation and intensity inhomogeneity aberrations in the input images. The proposed model is assessed qualitatively using visual comparisons and quantified using the relevant statistical measures. The experimental results confirm that the proposed model outperforms the existing methods in terms of restoration and contrast enhancement of speckled images. The proposed method also has shown the full potential to adapt the model to restore the degraded images following any distribution.