Image Denoising Algorithm Using Adaptive Shrinkage Threshold Based on Shearlet Transform
Xi Chen, Hui Sun, Chengzhi Deng · 2009
Threshold selection is the critical issue in image denoising. This paper deal with a new multiscale directional representation called the shearlet transform that has shown to represent specific classes of images with distributed discontinuities optimally. Techniques based on this transform for denoising using an efficient adaptive shrinkage threshold are presented. The shearlet transform not only provides the mean to detect orientations and to lead to sparse representations, but is moreover equipped with a rich mathematical structure similar to wavelets. Experiments show that this novel approach is very competitive for the purpose of image denoising.