An efficient systolic array grid-based structure of the robust Bayesian regularization technique for real-time enhanced imaging in uncertain remote sensing environment

Abstract

In this paper, we address a hardware implementation of the efficient robust Bayesian regularization architecture for the real-time enhancement of large-scale remote sensing (RS) imaging. The efficient sense of the proposed architecture is related to the high-performance embedded implementation that is achieved with the aggregation of parallel computing and systolic array design techniques in a novel grid connected-based accelerator. Then, the developed high-speed accelerator is integrated with an embedded processor via the HW/SW co-design paradigm. The presented approach is used for solving RS image enhancement/reconstruction of the ill-conditioned inverse spatial spectrum pattern estimation problems via an interesting low-cost high-performance embedded computing solution. Finally, we show the achieved results and how we drastically reduced the computational load for real-world large-scale geospatial images.