数字信号/图像反卷积问题的一种SURE方法
A SURE Approach for Digital Signal/Image Deconvolution Problems
摘要：
在本文中，我们感兴趣的是恢复数据退化的卷积和白高斯噪声的经典问题。所提出的方法的独创性是双重的。首先，我们制定的恢复问题作为一个非线性估计问题，导致最小化的标准来自斯坦的无偏二次风险估计。其次，使用可能是过完备的或不是过完备的任何分析和合成帧来执行去卷积过程。在这项工作中还提供了新的理论结果有关的斯坦的风险估计的方差的计算。在自然图像上进行的仿真表明，我们的方法w.r.t.传统的基于小波的恢复方法。
英文摘要：
In this paper, we are interested in the classical problem of restoring data degraded by a convolution and the addition of a white Gaussian noise. The originality of the proposed approach is two-fold. Firstly, we formulate the restoration problem as a nonlinear estimation problem leading to the minimization of a criterion derived from Stein's unbiased quadratic risk estimate. Secondly, the deconvolution procedure is performed using any analysis and synthesis frames that can be overcomplete or not. New theoretical results concerning the calculation of the variance of the Stein's risk estimate are also provided in this work. Simulations carried out on natural images show the good performance of our method w.r.t. conventional wavelet-based restoration methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
