Blind Compressed Sensing Over a Structured Union of Subspaces

—This paper addresses the problem of simultaneous signal recovery and dictionary learning based on compressive measurements. Multiple signals are analyzed jointly, with multiple sensing matrices, under the assumption that the unknown signals come from a union of a small number of disjoint subspaces. This problem is important, for instance, in image inpainting applications, in which the multiple signals are constituted by (incomplete) image patches taken from the overall image. This work extends standard dictionary learning and block-sparse dictionary optimization, by considering compressive measurements (e.g., incomplete data). Previous work on blind compressed sensing is also generalized by using multiple sensing matrices and relaxing some of the restrictions on the learned dictionary. Drawing on results developed in the context of matrix completion, it is proven that both the dictionary and signals can be recovered with high probability from compressed measurements. The solution is...

• Type: Journal
• Year: 2011
• Where: CORR
• Authors: Jorge Silva, Minhua Chen, Yonina C. Eldar, Guillermo Sapiro, Lawrence Carin
• Keywords: CORR 2011, Education, Image Inpainting, Incomplete Image, Linear Transformations