No abstract

To suppress the aperture diffraction and spectral leakage effects in the reconstruction process of the digital hologram and to maintain the original information recorded in the hologram, a novel reconstruction method based on extension and apodization of the digital hologram is presented, by which the original hologram can be extended by filling the average intensity values of the boundary, and the extended hologram is apodized by use of the constructed window function. As a sample, the digital hologram of the static particle field is recorded and numerically extended and then apodized with the appointed window. Finally, an unabridged and clear digital holographic image is reconstructed from the extended and apodized hologram. The experimental results confirm that this method cannot only eliminate the black-and-white diffraction fringes in the reconstructed image, but also attain the unabridged image with high quality.

A novel approach is proposed for measuring a surface's bidirectional reflectance distribution function rapidly and accurately. By using a hemi-parabolic mirror, the angular distribution of a surface's reflectance in three-dimensional space can be transformed into a two-dimensional planar image, which is collected by a CCD camera and goes through a followed coordinate mapping. It is shown that, using this method and apparatus, measurement of in plane and out of plane reflectance distributions may be realized within two minutes.

Digital holographic microscopy enables the measurement of the quantitative light field information and the visualization of transparent specimens. It can be implemented for complex amplitude imaging and thus for the investigation of biological samples including tissues, dry mass, membrane fluctuation, etc. Currently, deep learning technologies are developing rapidly and have already been applied to various important tasks in the coherent imaging. In this paper, an optimized structural convolution neural network PhaseNet is proposed for the reconstruction of digital holograms, and a deep learning-based holographic microscope using above neural network is implemented for quantitative phase imaging. Living mouse osteoblastic cells are quantitatively measured to demonstrate the capability and applicability of the system.

In digital holography, the coherent noise affects the measurement accuracy and reliability greatly due to the high spatial and temporal coherence of the laser. Especially, compared with the speckle noise of intensity in digital holography, the coherent noise of phase contains more medium- and low-frequency characteristics, which hinders the effectiveness of noise suppression algorithms. Here, we propose a single-shot untrained self-supervised network (SUSNet) for the coherent noise suppression of phase, requiring only one noisy phase map to complete the optimization and learning. The SUSNet can smoothen and suppress the background fluctuations, parasitic fringes, and diffraction loops in a noisy phase and shows good generalization performance for samples with different shapes, sizes, and phase ranges. Compared with the traditional algorithms and the ground truth-supervised neural network (DnCNN), the SUSNet has the best noise suppression performance and background smoothing effect. As a result, the SUSNet can suppress the fluctuation range to ∼20% of the original range.