基于空间自适应非凸正则项全变差相干斑噪声抑制

摘要

Total variation is a hot point of research on speckle reduction. The nonconvex regularizer can better preserve or even enhance the information about the edges of an image. Spatially adaptive regularization parameters can reasonably control the level of speckle reduction according to the region in which the pixels are, and improve the speckle reduction effect. In this paper, we present a new total variation model for speckle reduction by integrating nonconvex regularizer and spatially adaptive regularization parameters. In order to solve the model, a new algorithm is designed based on Newton’s method, augmented Lagrange multiplier, alternating direction method of multipliers, and iteratively reweighted method. The numerical examples demonstrate that the proposed model can obtain the better speckle reduction effect.%全变差正则化方法是相干斑噪声抑制研究的热点。非凸正则项能够更好地保持图像的边缘、纹理细节信息；空间自适应正则化参数可以根据像素点所在的区域，合理地控制噪声抑制程度，从而提高噪声抑制效果。本文结合非凸正则项和空间自适应正则化参数提出了一种新的全变差相干斑噪声抑制模型，并且给出了一种模型求解的数值算法。数值试验结果表明该模型能够获得更好的相干斑噪声抑制效果。