Single-shot pixel super-resolution phase imaging by wavefront separation approach

Abstract

We propose a novel approach for lensless single-shot phase retrieval, which provides pixel super-resolution phase imaging. The approach is based on a computational separation of carrying and object wavefronts. The imaging task is to reconstruct the object wavefront, while the carrying wavefront corrects the discrepancies between the computational model and physical elements of an optical system. To reconstruct the carrying wavefront, we do two preliminary tests as system calibration without an object. Essential for phase retrieval noise is suppressed by a combination of sparse- and deep learning-based filters. Robustness to discrepancies in computational models and pixel super-resolution of the proposed approach are shown in simulations and physical experiments. We report an experimental computational super-resolution of 2μm, which is 3.45× smaller than the resolution following from the Nyquist-Shannon sampling theorem for the used camera pixel size of 3.45μm. For phase bio-imaging, we provide Buccal Epithelial Cells reconstructed with a quality close to the quality of a digital holographic system with a 40× magnification objective. Furthermore, the single-shot advantage provides a possibility to record dynamic scenes, where the frame rate is limited only by the used camera. We provide amplitude-phase video clip of a moving alive single-celled eukaryote.