Abstract
Deconvolution of native radio interferometric images constitutes a major computational component of the imaging process. An efficient and robust deconvolution operation is essential for reconstruction of the true sky signal from measured telescopic data. The techniques of compressed sensing provide a mathematically-rigorous framework within which to implement deconvolution of images formed from a sparse set of nearly-random measurements. We present an accelerated implementation of the orthogonal matching pursuit (OMP) algorithm (a compressed sensing method) that makes use of graphics processing unit (GPU) hardware. We show that OMP correctly identifies more sources than CLEAN, identifying up to 82% of the sources in 100 test images, while CLEAN only identifies up to 61% of the sources. In addition, the residual after source extraction is [Formula: see text] times lower for OMP than for CLEAN. Furthermore, the graphics implementation of OMP performs around 23 times faster than a 4-core CPU.
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