论文标题
图形盲解卷积和稀疏约束
Graph Blind Deconvolution with Sparseness Constraint
论文作者
论文摘要
我们提出了一种用于图上信号的盲解方法，对原始信号的确切稀疏约束。图形盲反卷积是一种算法，用于从一组模糊和嘈杂的测量值中估算图上的原始信号。对于许多不同的应用程序，需要对非零元素的数量施加限制。本文处理了对原始资源确切数量的约束的问题，该问题由$ \ ell_0 $ norm限制的优化问题给出。我们使用ADMM迭代求解器解决了这个非凸优化问题。使用合成信号的数值实验证明了该方法的有效性。
We propose a blind deconvolution method for signals on graphs, with the exact sparseness constraint for the original signal. Graph blind deconvolution is an algorithm for estimating the original signal on a graph from a set of blurred and noisy measurements. Imposing a constraint on the number of nonzero elements is desirable for many different applications. This paper deals with the problem with constraints placed on the exact number of original sources, which is given by an optimization problem with an $\ell_0$ norm constraint. We solve this non-convex optimization problem using the ADMM iterative solver. Numerical experiments using synthetic signals demonstrate the effectiveness of the proposed method.
