Lensfree auto-focusing imaging using nuclear norm of gradient

Abstract

Auto-focusing is an essential task for lensfree holographic microscopy, where the optimal focal plane needs to be determined from refocused Z-stack data. In this work, we propose a new auto-focusing criterion, nuclear norm of gradient (NoG), to enhance the accuracy of distance estimation for lensfree imaging. The NoG metric is composed of three parts: shape acquisition, gradient calculation, and gradient fusion. The experiment is conducted in a multi-height on-chip system, where 20 sets of samples including resolution target, stained slide, and label-free cell are employed. The experimental results demonstrate that the accuracy of the NoG metric on auto-focusing is superior to state-of-the-art metrics. Also, the experiment of a multi-layer target proves that the NoG metric has a good optical-sectioning capability. We believe that the NoG metric is expected to be a useful tool for lensfree on-chip microscopes.