Reconstruction of Ridgelet Coefficients using Total Variation Minimization

摘要：

The combination of ordinary wavelet shrinkage with total variation minimization was successfully applied. In this paper, we apply the technique with respect to ridgelet coefficients. Firstly, a translation-invariant ridgelet transform is proposed. And then, an image denoising algorithm, based on ridgelet shrinkage and total variation minimization, is given. This algorithm preserves the important information of image and reduces the noise by thresholding small ridgelet coefficients. By replacing these thresholded coefficients by values minimizing the total variation, the algorithm reduces the pseudo-Gibbs artifacts. Experiment results show that this algorithm yields significantly superior image quality and higher peak signal to noise ratio (PSNR).

作者: Chengzhi DENG Hanqiang CAO Shengqian WANG

作者单位: Huazhong University of Science and Technology, China Jiangxi Science and Technology Normal College, China

会议类型: 国际会议

会议名称: 2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)

会议地点: 哈尔滨

会议语种:英文

在线出版日期: 2007-05-23（万方平台首次上网日期，不代表论文的发表时间）