Poisson Noise Removal Using Non-convex Total Generalized Variation

As is well-known that the total generalized variation model performs well in reducing the staircasing effect while removing noise, but it tends to cause the undesirable edge details blurring. To overcome this drawback, the current paper introduces the non-convex restriction into the total generalized variation regularizer and constructs an improved edge-preserving optimization model for Poissonian images restoration. For solving the minimization problem, we propose an efficient alternating minimization method by skillfully combining the classical iteratively reweighted l(1) algorithm and primal-dual framework. Some visual experiments presented in the illustration section, which are compared with some related denoising methods, demonstrate the better performance of the developed scheme in staircase artifacts reduction and image features protection. Besides, the measurable comparisons also indicate that our outcomes enjoy the best restoration accuracy against other popular competitors.