Pupil phase encoding for multi-aperture imaging

Digital super-resolution refers to computational techniques that exploit the generalized sampling theorem to extend image resolution beyond the pixel spacing of the detector, but not beyond the optical limit (Nyquist spatial frequency) of the lens. The approach to digital super-resolution taken by the PERIODIC multi-lenslet camera project is to solve a forward model which describes the effects of sub-pixel shifts, optical blur, and detector sampling as a product of matrix factors. The associated system matrix is often ill-conditioned, and convergence of iterative methods to solve for the high-resolution image may be slow. We investigate the use of pupil phase encoding in a multi-lenslet camera system as a means to physically precondition and regularize the computational super-resolution problem. This is an integrated optical-digital approach that has been previously demonstrated with cubic type and pseudo-random phase elements. Traditional multi-frame phase diversity for imaging through atmospheric turbulence uses a known smooth phase perturbation to help recover a time series of point spread functions corresponding to random phase errors. In the context of a multi-lenslet camera system, a known pseudo-random or cubic phase error may be used to help recover an array of unknown point spread functions corresponding to manufacturing and focus variations among the lenslets.