Nonlinear image reconstruction based upon sparse representations of images has recently received widespread attention
with the emerging framework of compressed sensing (CS). This theory indicates that, when feasible, judicious selection
of the type of distortion induced by measurement systems may dramatically improve our ability to perform image reconstruction.
However, applying compressed sensing theory to practical imaging systems poses a key challenge: physical
constraints typically make it infeasible to actually measure many of the random projections described in the literature, and
therefore, innovative and sophisticated imaging systems must be carefully designed to effectively exploit CS theory. In
video settings, the performance of an imaging system is characterized by both pixel resolution and field of view. In this
work, we propose compressive imaging techniques for improving the performance of video imaging systems in the presence
of constraints on the focal plane array size. In particular, we describe a novel yet practical approach that combines
coded aperture imaging to enhance pixel resolution with superimposing subframes of a scene onto a single focal plane
array to increase field of view. Specifically, the proposed method superimposes coded observations and uses wavelet-based
sparsity recovery algorithms to reconstruct the original subframes. We demonstrate the effectiveness of this approach by
reconstructing with high resolution the constituent images of a video sequence.