Pixel super-resolution phase retrieval algorithms for digital holography

Abstract

The imaging quality of in-line digital holography is challenged by the twin-image and aliasing effects because sensors only respond to intensity and pixels are of finite size. As a result, phase retrieval and pixel superresolution techniques serve as the two essential ingredients for high-fidelity holographic imaging. In this work, we combine the two within a unified algorithmic framework. Pixel super-resolution phase retrieval is recast as an optimization problem and is solved via gradient descent-based algorithms. Regularization techniques and Nesterov’s momentum are introduced to further speed up data acquisition and iterative reconstruction. The proposed algorithms are verified through a proof-of-concept lensless on-chip microscope. We demonstrate experimentally the capability of pixel super-resolution phase retrieval techniques in revealing the subpixel and quantitative phase information of complex biological samples.