A new adaptive boosting total generalized variation (TGV) technique for image denoising and inpainting

摘要

In this paper we present a new adaptive boosting technique for total generalized variation (TGV) based image denoising and inpainting. Instead of the strengthening and substracting steps in existing boosting techniques, the proposed technique is iteratively operated by two steps: the first step is to take average of restored image with observed image, and updated parameter; the second step is to operate the TGV restoration algorithm with the average and dynamic parameter. For each iteration, as the input contains more correct information, the restoration algorithm can produce signals with more details. We have solved our boosting TGV model by primal-dual method, and applied the boosting TGV technique for gray/color image denoising and inpainting. Our algorithms have been discussed about influence of parameters, computational cost and compared with several typical existing methods. Plenty of experimental results show that our method can produce images with more structures and prevent staircase artifacts effectively. (C) 2019 Elsevier Inc. All rights reserved.