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.