Compressive coded aperture superresolution image reconstruction

Recent work in the emerging field of compressive sensing indicates that, when feasible, judicious selection of the type of distortion induced by measurement systems may dramatically improve our ability to perform reconstruction. The basic idea of this theory is that when the signal of interest is very sparse (i.e., zero-valued at most locations) or compressible, relatively few incoherent observations are necessary to reconstruct the most significant non-zero signal components. However, applying this theory to practical imaging systems is challenging in the face of several measurement system constraints. This paper describes the design of coded aperture masks for super-resolution image reconstruction from a single, low-resolution, noisy observation image. Based upon recent theoretical work on Toeplitz-structured matrices for compressive sensing, the proposed masks are fast and memory-efficient to compute. Simulations demonstrate the effectiveness of these masks in several different settings.