Near real time enhancement of geospatial imagery via systolic implementation of neural network-adapted convex regularization techniques

Authors:
Y. Shkvarko;A. Castillo Atoche;D. Torres-Roman
Affiliations:
Department of Telecommunications, CINVESTAV (Centro de Investigaciones y Estudios Avanzados del IPN), Unidad Guadalajara, Mexico;Department of Telecommunications, CINVESTAV (Centro de Investigaciones y Estudios Avanzados del IPN), Unidad Guadalajara, Mexico and Department of Mechatronics, Autonomous University of Yucatan, M ...;Department of Telecommunications, CINVESTAV (Centro de Investigaciones y Estudios Avanzados del IPN), Unidad Guadalajara, Mexico
Venue:
Pattern Recognition Letters
Year:
2011

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Abstract

In this paper, we address a new approach for near-real-time enhancement of large-scale Geospatial and aerial remote sensing (RS) imagery that aggregates descriptive and Bayesian convex regularization paradigms for solving the image reconstruction inverse problems with efficient systolic-based neural network (NN) computing. This task is approached via Hardware-Software (HW/SW) codesign oriented at the Field Programmable Gate Array (FPGA) digital implementation that unifies the NN-adapted image enhancement/reconstruction techniques with a novel efficient computational architecture based on a Network of Systolic Arrays (NSA). We demonstrate how such unification reduces drastically the computational load of the real-world RS image enhancement/reconstruction tasks resulting in efficient numerical algorithms suitable for quasi real-time NN-adapted implementation with the existing generation of the FPGA-based digital processors that implement the proposed NSA computational architecture.