Regularization Parameter Selection for Total Variation Model Based on Local Spectral Response

Abstract

In the past decades, various image regularization methods have been introduced. Among them, total variation model has drawn much attention for the reason of its low computational complexity and well-understood mathematical behavior. However, regularization parameter estimation of total variation model is still an open problem. To deal with this problem, a novel adaptive regularization parameter selection scheme is proposed in this paper, by means of using the local spectral response, which has the capability of locally selecting the regularization parameters in a content-aware way and therefore adaptively adjusting the weights between the two terms of the total variation model. Experiment results on simulated and real noisy image show the good performance of our proposed method, in visual improvement and peak signal to noise ratio value.

Efficient Approaches to Computer Vision and Pattern Recognition

“…Nonetheless, estimating the regularization parameters for the total variation model remains a task to be solved. Zheng et al [16] proposed a novel adaptive regularization parameter selection scheme by means of local spectral response in a content-aware way. The experiment results showed that, relatively, their proposal can yield satisfying denoised images with higher PSNR values and lower time consumption.…”