Real-time quantitative phase imaging is beneficial for observation and analysis of living cells. Despite off-axis interferometry-based quantitative phase microscopy (off-axis QPM) offers single-shot image acquisition, it usually requires a calibration image captured at a blank field of view to correct the aberration and a multi-step processing algorithm to reconstruct a phase map. Therefore, it is challenging to achieve real-time phase imaging. To simplify experimental operations and expedite image processing, we propose a lightweight U-Net based deep neural network for calibration-free and fast phase retrieval in off-axis QPM. Output phase maps of the lightweight U-Net achieve high fidelity with an average Structural SIMilarity (SSIM) index value of 90.2%. Via running this lightweight U-Net model on a laptop connected with a portable QPM system, we demonstrate an ease-of-use and compact QPM method that can be used for real-time imaging of living cells.
Deep learning techniques are always bound with big data and large, sophisticated models. In this paper, we show that this is not necessarily true for the task of end-to-end phase retrieval in off-axis interferometric quantitative phase imaging. For this task, we first introduce a new loss function, called bucket error rate (BER), for addressing the problem of imbalanced data distribution by balancing loss-bias of target and background area adaptively. With BER, we demonstrate that a U-Net model can learn the underneath logic for converting a raw interferogram to a phase map from only one training sample. At last, we present a novel mixed-context network (MCN) which can simultaneously aggregate local- and global-contextual information. Experimental results show that compared to U-Net, the proposed MCN is more accurate, more compact, and can be trained faster.
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