# HoloForkNet: Digital Hologram Reconstruction via Multibranch Neural Network
## 1 Introduction  
Digital holography is a technique that allows for recording, processing and reconstructing data about 2D and 3D scenes [1; 2]. This became possible due to the development of optical-digital methods of image registration and processing. At present, digital holography is widely used in medicine [3; 4; 5], biology [6; 7; 8; 9], ecology [10; 11], metrology [6; 12], astronomy [13] and other fields. A digital hologram is an interference pattern registered from reference and object beams coherent with each other. Object images are typically reconstructed numerically using light propagation modeling [14; 15]. The reconstructed object image can be viewed as a 2D section or as a 3D scene. Images can also be reconstructed optically by displaying holograms on spatial light modulators (SLMs) [16; 17]. Digital holography has great potential for application in various fields of science and technology [2], including artificial-intelligence-based methods [18; 19; 20; 21]. The main problems of digital holography include the presence of zero-order twin images [22; 23; 24] and optical noises [1; 25; 26; 27] in reconstructed images. These factors can lead to a decrease in the quality of reconstruction and, therefore, the accuracy of the obtained object images. Optical noises may occur due to imperfections in the optical elements, phase shift, or non-uniform intensity in the illuminating light. Zero-order and twin images are parasitic diffraction orders. They are a significant negative factor in the case of inline digital holography [28]. Off-axis holography [29] allows for a reduction in the effect of zero-order and twin images due to the spatial separation of diffraction orders. However, this scheme reduces the amount of registered object information. To reduce the effect of noise in inline holography, the most popular method is the registration of several (usually four) holograms with different phase shifts [1; 28]. Another option is time-consuming andoften difficult to implement iterative procedures, including regularized inversion, model fitting and compressive sensing [30; 31; 32; 33]. The development of computer technology allowed for the processing of large datasets, which led to the development of neural network-based algorithms for various tasks. In particular, in holography, neural network-based methods can be used for reconstruction acceleration [34], denoising [35; 36; 37], aberrations compensation [38; 39], suppression of parasitic diffraction orders [40; 41], data compression [42], particle localization, positioning [43; 44] and classification [45], depth prediction and autofocusing [46; 47], etc. For holographic image reconstruction, the focus is mainly on obtaining a single amplitude or phase [48; 49; 50] image or two images: amplitude/phase [36; 51; 52; 53; 54; 55; 56; 57] and extended focused imaging/depth map ones [57; 58], in a single [57; 58; 59; 60; 51; 52; 56; 57] or multiwavelength [55] scheme. Typically, neural networks for 3D hologram reconstruction contain one decoder branch, reconstructing not object planes separately but a depth map where objects from different planes are separated by their intensity. However, reconstructing 3D scene planes from a single hologram would optimize and speed up the reconstruction, enhance the capabilities of deep learning holography and improve the image quality of the whole scene. It is especially useful for inline holograms, where the overlapping of objects on different 2D planes and noise significantly reduce the reconstruction quality. HoloForkNet is the proposed neural network for holographic image reconstruction. It is a deep convolutional neural network with skip connections. We propose the use of a multibranch architecture, which is used in various fields of science [59; 60]. To our knowledge, the idea of using a multibranch neural network for digital hologram reconstruction was first described in [51]. The amplitude and phase of the original object were reconstructed simultaneously.