GAN-Holo: Generative Adversarial Networks-Based Generated Holography Using Deep Learning

Current development in a deep neural network (DNN) has given an opportunity to a novel framework for the reconstruction of a holographic image and a phase recovery method with real-time performance. There are many deep learning-based techniques that have been proposed for the holographic image reconstruction, but these deep learning-based methods can still lack in performance, time complexity, accuracy, and real-time performance. Due to iterative calculation, the generation of a CGH requires a long computation time. A novel deep generative adversarial network holography (GAN-Holo) framework is proposed for hologram reconstruction. This novel framework consists of two phases. In phase one, we used the Fresnel-based method to make the dataset. In the second phase, we trained the raw input image and holographic label image data from phase one acquired images. Our method has the capability of the noniterative process of computer-generated holograms (CGHs). The experimental results have demonstrated that the proposed method outperforms the existing methods.

Registration of the dispersed composition of aerosol media by the holographic method

The article describes a device based on the holographic method for measuring the parameters of dispersed aerosols. In the proposed device, the measured particle is irradiated with two beams perpendicular to the main radiation axis, while the resulting holographic image in each of the projections gives an increased amount of information (in contrast to existing solutions) about the parameters of the particles. The information obtained is processed layer by layer using digital holography methods to form a volumetric representation of the aerosol under study, which significantly increases the information content of measurements in comparison with existing devices. Methods and algorithms for layer-by-layer processing of the obtained holographic images are described, which make it possible to reconstruct the parameters of aerosols of complex shapes. The design of the device and an algorithm for layer-by-layer reconstruction of aerosol images are proposed.