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Optimization-based approaches for image deblurring and denoising on Graphics Processing Units (GPU) are considered. In particular, a new GPU implementation of a recent gradient projection method for edge-preserving removal of Poisson noise is presented. The speedups over standard CPU implementations are evaluated on both synthetic data and astronomical and medical imaging problems.
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