In this paper, we analyze the Bregman iterative model using the G-norm. Firstly, we show the convergence of the iterative model. Secondly, using the source condition and the symmetric Bregman distance, we consider the error estimations between the iterates and the exact image both in the case of clean and noisy data. The results show that the Bregman iterative model using the G-norm has the similar good properties as the Bregman iterative model using the L2-norm.
In this paper, we propose a new time-dependent model for solving total variation （TV） minimization problems in image restoration. The main idea is applying a priori smoothness on the solution image. Five different BCs are introduced and analyzed. 2D numerical experimental results by explicit numerical schemes are discussed.
