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Digital holographic microscopy (DHM) enables the three-dimensional (3D) reconstruction of quantitative phase distributions from a defocused hologram. Traditional computational algorithms follow a sequential approach in which one first reconstructs the complex amplitude distribution and later applies focusing algorithms to provide an in-focus phase map. In this work, we have developed a synergistic computational framework to compensate for the linear tilt introduced in off-axis DHM systems and autofocus the defocused holograms by minimizing a cost function, providing in-focus reconstructed phase images without phase distortions. The proposed computational tool has been validated in defocused holograms of human red blood cells and three-dimensional images of dynamic sperm cells. 

We have developed a joint phase compensation and autofocusing method for telecentric off-axis Digital Holographic Microscopy (DHM), providing in-focus reconstructed phase images without phase distortions. 

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. 

This works presents a reconstruction algorithm to recover the complex object information for an off-axis digital holographic microscope (DHM) operating in the telecentric regimen. We introduce an automatic and fast method to minimize a cost function that finds the best numerical conjugated reference beam to compensate the filtered object information, eliminating any undesired phase perturbation due to the tilt between the reference and object waves. The novelties of the proposed approach, to the best of our knowledge, are a precise estimation of the interference angle between the object and reference waves, reconstructed phase images without phase perturbations, and reduced processing time. The method has been validated using a manufactured phase target and biological samples. 

A convolutional autoencoder for complete phase reconstruction in Digital Holographic Microscopy is reported. After proper training, this computationally efficient method reconstructs DHM holograms accurately when compared to the traditional approaches. This learning-based method is trained and validated with experimental samples of red blood cells. 

We present a reconstruction algorithm for digital holographic microscopy (DHM) operating in the telecentric regimen. This strategy rests on the minimization of a cost function to estimate both the numerical reference wave and the quantitative phase information. This algorithm paves the way to a universal DHM reconstruction tool. 

We present a single-shot image processing method to reduce speckle noise mitigating undesired blurring effects in both amplitude and phase reconstructed images. The implementation is based on the average of multiple median filtered images with different kernel size. This approach provides final reconstructed amplitude and phase images with reduced speckle contrast and without penalizing the spatial resolution.