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

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...

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Bibliographic Details
Published in:Journal of real-time image processing 2017-12, Vol.13 (4), p.783-796
Main Authors: Castillo Atoche, A., Carrasco Alvarez, R., Palma Marrufo, O., Vázquez Castillo, J.
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Arrays
Bayesian analysis
Co-design
Computer architecture
Computer Graphics
Computer Science
Data processing
Embedded systems
Field programmable gate arrays
Image enhancement
Image Processing and Computer Vision
Image reconstruction
Inverse problems
Microprocessors
Multimedia Information Systems
Original Research Paper
Pattern Recognition
Real time
Regularization
Remote sensing
Robustness
Signal processing
Signal,Image and Speech Processing
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Summary: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.
ISSN:1861-8200
1861-8219
DOI:10.1007/s11554-014-0441-y