Overcomplete pre-learned dictionary for incomplete data SAR imaging towards pervasive aerial and satellite vision.

Nowadays, pervasive vision through synthetic aperture radar (SAR) imaging sensors is a crucial part of air-borne and space-borne platforms to reach the concept of internet of multimedia things over satellites and visual flying ad-hoc networks. SAR sensors always need high-performance data reconstruction techniques to provide quality of experience for end-users of the distributed surveillance nodes and ensuring reliable decision-making in the autonomous vehicles. Using prior information in SAR image reconstruction, improves the quality of reconstructed image. Many sparse regularization methods use pre information terms in which the image is sparsely presented based on a predefined dictionary. However, if the desired features of the real image have not a sparse representation based on the predefined dictionary, image reconstruction with enhanced interested features will be failed. Dictionary learning to better adapt with the underlying scene can lead to better image reconstruction. On the other hand, using the idea of dictionary learning in the image reconstruction problem, and considering that the data used to train the dictionary may be incomplete and noisy, will create limitations for this method. In this paper, a new idea based on the use of an overcomplete dictionary consisting of a learned dictionary and a predefined dictionary (pre-learned dictionary) for SAR image reconstruction problem is presented. Developing the necessary mathematical relations and providing the framework for SAR image reconstruction based on the use of the overcomplete pre-learned dictionary, to simultaneously enhance the features considered in advance and the features associated with the image, from incomplete and noisy SAR raw data is presented in this article. We also present an iterative algorithm for solving the corresponding optimization problem. Simulation results based on the real data of TerraSAR-X and the Lynx airborne system show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]