Auto-focusing and quantitative phase imaging using deep learning for the incoherent illumination microscopy system

It is well known that the quantitative phase information which is vital in the biomedical study is hard to be directly obtained with bright-field microscopy under incoherent illumination. In addition, it is impossible to maintain the living sample in focus over long-term observation. Therefore, both the autofocusing and quantitative phase imaging techniques have to be solved in microscopy simultaneously. Here, we propose a lightweight deep learning-based framework, which is constructed by residual structure and is constrained by a novel loss function model, to realize both autofocusing and quantitative phase imaging. It outputs the corresponding in-focus amplitude and phase information at high speed (10fps) from a single-shot out-of-focus bright-field image. The training data were captured with a designed system under a hybrid incoherent and coherent illumination system. The experimental results verify that the focused and quantitative phase images of non-biological samples and biological samples can be reconstructed by using the framework. It provides a versatile quantitative technique for continuous monitoring of living cells in long-term and label-free imaging by using a traditional incoherent illumination microscopy system. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

In this work, a lightweight deep learning-based framework is proposed for both autofocusing and quantitative phase imaging in microscopy. The framework utilizes a residual structure and a novel loss function model to output in-focus amplitude and phase information at high speed from a single out-of-focus bright-field image. Experimental results demonstrate the effectiveness of the framework in reconstructing focused and quantitative phase images of both non-biological and biological samples, providing a versatile quantitative technique for continuous monitoring of living cells in long-term imaging.