对于全变分降噪模型, Split Bregman算法收敛快而且具有较好的降噪效果.通过研究Split Bregman算法, 提出了一种自动调节拉格朗日乘子和罚参数的自适应Split Bregman算法.实验结果表明, 新方法与传统Split Bregman算法相比具有更快的收敛速度, 同时能在保持红外图像边缘特性的前提下有效地去除噪声.

Split Bregman method, which can converge quickly and denoise very well, is considered to be an extremely efficient method for total variation denosing model. By studying Split Bregman method, an adaptive Split Bregman method which can adjust Lagerange multiplier and penalty parameter automatically was proposed. Experimental results show that the new method can not only converge faster than the traditional Split Bregman method, but also preserve the edge information while removing noises.