Adaptive Regularization Method Based Total Variational De-noising Algorithm
基于自适应正则化的全变分去噪算法

Stanley Osher and Martin Burger introduced an iterative regularization method for image de-nosing based on the Bregman distance. The approach can improve the general procedure and save the execution time. However, important information, such as texture is often compromised in the process of de-noising. The reason is that the proposed approach ignored the gradient information of each pixel. In order to avoid the above phenomenon, a novel texture preserving variational de-noising method based on the use of adaptive regularization is proposed in this paper . The new adaptive regularization method based total variational de-noising algorithm uses an adaptive fidelity term which locally controls the extent of de-nosing over image regions according to the gradient information of each pixel. So important information, such as edge and texture is preserved. The numerical results for de-nosing show the improvement in the signal-to-noise ratio (SNR) over standard model processes, and they are visually more appealing.