A Fast Algorithm for Image Deblurring with Total Variation Regularization

摘要: We propose and test a simple algorithmic framework for recovering images from blurry noisy observations based on total variation (TV) regularization when blurring point-spread function is given. Using splitting technique, we construct an iterative procedure of alternately solving pair easy subproblems associated with increasing sequence penalty parameter values. The main computation at each iteration three Fast Fourier Transforms (FFTs). present numerical results showing that rudimentary implementation our algorithm already performs favorably in comparison two the existing start-of-the-art algorithms. In particular, it runs orders magnitude faster than number algorithms TVL2-based de-convolution problems to good accuracies.