GPU-Accelerated Algorithms for Compressed Signals Recovery with Application to Astronomical Imagery Deblurring

TL;DR

This paper introduces GPU-accelerated algorithms leveraging circulant matrix properties for efficient, large-scale sparse signal recovery, demonstrated through astronomical image deblurring in the compressed domain.

Contribution

The work presents novel parallel algorithms that reduce memory usage and significantly speed up sparse signal recovery using GPUs, specifically tailored for circulant matrices.

Findings

Achieved tenfold speedup in signal recovery.

Enabled recovery of very large signals with limited memory.

Successfully applied to astronomical image deblurring.

Abstract

Compressive sensing promises to enable bandwidth-efficient on-board compression of astronomical data by lifting the encoding complexity from the source to the receiver. The signal is recovered off-line, exploiting GPUs parallel computation capabilities to speedup the reconstruction process. However, inherent GPU hardware constraints limit the size of the recoverable signal and the speedup practically achievable. In this work, we design parallel algorithms that exploit the properties of circulant matrices for efficient GPU-accelerated sparse signals recovery. Our approach reduces the memory requirements, allowing us to recover very large signals with limited memory. In addition, it achieves a tenfold signal recovery speedup thanks to ad-hoc parallelization of matrix-vector multiplications and matrix inversions. Finally, we practically demonstrate our algorithms in a typical application of circulant matrices: deblurring a sparse astronomical image in the compressed domain.