Numerical dark field illumination applied to experimental digital lensless holographic microscopy for reconstructions with enhanced contrast

Resolution and Contrast Enhancement for Lensless Digital Holographic Microscopy and Its Application in Biomedicine

An important imaging technique in biomedicine, the conventional optical microscopy relies on relatively complicated and bulky lens and alignment mechanics. Based on the Gabor holography, the lensless digital holographic microscopy has the advantages of light weight and low cost. It has developed rapidly and received attention in many fields. However, the finite pixel size at the sensor plane limits the spatial resolution. In this study, we first review the principle of lensless digital holography, then go over some methods to improve image contrast and discuss the methods to enhance the image resolution of the lensless holographic image. Moreover, the applications of lensless digital holographic microscopy in biomedicine are reviewed. Finally, we look forward to the future development and prospect of lensless digital holographic technology.

Linear diattenuation imaging of biological samples with digital lensless holographic microscopy

A digital lensless holographic microscope (DLHM) sensitive to the linear diattenuation produced by biological samples is reported. The insertion of a linear polarization-states generator and a linear polarization-states analyzer in a typical DLHM setup allows the proper linear diattenuation imaging of microscopic samples. The proposal has been validated for simulated and experimental biological samples containing calcium oxalate crystals extracted from agave leaves and potato starch grains. The performance of the proposed method is similar to that of a traditional polarimetric microscope to obtain linear diattenuation images of microscopic samples but with the advantages of DLHM, such as numerical refocusing, cost effectiveness, and the possibility of field-portable implementation.

Preprocessing in digital lensless holographic microscopy for intensity reconstructions with enhanced contrast

In this work, a numerical method to enhance the contrast of intensity hologram reconstructions of digital lensless holographic microscopy (DLHM) is presented. The method manipulates the in-line hologram and reference images through mathematical operations between them; additionally, a sharpening operation, functionalized in terms of the parameters of the recording setup, is applied to the said images. The preprocessing of the recorded images produces a modified in-line hologram and a reference wave image from which an intensity reconstruction with a 25% improvement of its contrast, with respect to the conventional reconstruction procedure, is achieved. The method is illustrated with intensity reconstructions of a hologram of a monolayer of polystyrene spheres 1.09 µm in diameter. Finally, the preprocessing method is validated with a modeled hologram, successfully applied to holograms of the section of the head a Drosophila melanogaster fly and its results are contrasted with those obtained via bright-field microscopy.

Numerical dark-field imaging using deep-learning

Dark-field microscopy is a powerful technique for enhancing the imaging resolution and contrast of small unstained samples. In this study, we report a method based on end-to-end convolutional neural network to reconstruct high-resolution dark-field images from low-resolution bright-field images. The relation between bright- and dark-field which was difficult to deduce theoretically can be obtained by training the corresponding network. The training data, namely the matched bright- and dark-field images of the same object view, are simultaneously obtained by a special designed multiplexed image system. Since the image registration work which is the key step in data preparation is not needed, the manual error can be largely avoided. After training, a high-resolution numerical dark-field image is generated from a conventional bright-field image as the input of this network. We validated the method by the resolution test target and quantitative analysis of the reconstructed numerical dark-field images of biological tissues. The experimental results show that the proposed learning-based method can realize the conversion from bright-field image to dark-field image, so that can efficiently achieve high-resolution numerical dark-field imaging. The proposed network is universal for different kinds of samples. In addition, we also verify that the proposed method has good anti-noise performance and is not affected by the unstable factors caused by experiment setup.