Abstract
The conventional reconstruction method of off-axis digital holographic microscopy (DHM) relies on computational processing that involves spatial filtering of the sample spectrum and tilt compensation between the interfering waves to accurately reconstruct the phase of a biological sample. Additional computational procedures such as numerical focusing may be needed to reconstruct free-of-distortion quantitative phase images based on the optical configuration of the DHM system. Regardless of the implementation, any DHM computational processing leads to long processing times, hampering the use of DHM for video-rate renderings of dynamic biological processes. In this study, we report on a conditional generative adversarial network (cGAN) for robust and fast quantitative phase imaging in DHM. The reconstructed phase images provided by the GAN model present stable background levels, enhancing the visualization of the specimens for different experimental conditions in which the conventional approach often fails. The proposed learning-based method was trained and validated using human red blood cells recorded on an off-axis Mach–Zehnder DHM system. After proper training, the proposed GAN yields a computationally efficient method, reconstructing DHM images seven times faster than conventional computational approaches.
Highlights
Quantitative phase imaging (QPI) techniques quantify the phase measurements from micrometer-sized biological and non-biological samples
To use our proposed learning-based method, a sample code to reconstruct quantitative free-of-aberration phase images from red blood cells (RBCs) holograms recorded in an off-axis telecentricbased digital holographic microscopy (DHM) is freely available in [56]
We report on a conditional generative adversarial network to fully reconstruct quantitative phase images from human red blood cells (RBCs)
Summary
Quantitative phase imaging (QPI) techniques quantify the phase measurements from micrometer-sized biological and non-biological samples. The DHM systems are based on optical interferometry to reconstruct both amplitude and phase distributions of biological and non-biological specimens, providing functional and morphological sample information. Owing to its high sensitivity, imaged field of view, and frame rate acquisition, DHM stands out among the QPI techniques to reconstruct high-resolution phase images from unstained samples [13]. The robustness and sensitivity of the phase measurements in some DHM systems within the nanometric range have allowed dynamic imaging, for instance, three-dimensional (3D) particle tracking [15,16], cell motility studies [17,18,19], and dynamic changes of surface topography [20,21]
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