# Deep-Learning Multiscale Digital Holographic Intensity and Phase Reconstruction
## 1 Introduction  
Digital holography is a technique that uses electronic components to record interference patterns (holograms) containing wavefront information of an observed object [1, 2]. These holograms are then reconstructed into object representations using a computer called holographic reconstruction [3, 4]. The computer simulates the diffraction of the hologram, generating the complex amplitude of the reconstructed object's optical wave and acquiring both the intensity and phase information of the measured object. This process ultimately leads to the reconstruction of a three-dimensional image [5], called holographic reconstruction. However, traditional holographic reconstruction often faces challenges, including speckle noise, zero-order and conjugate images, and wrapped phase [6, 7], making the reconstruction process complex and producing average image quality.  
With the rapid development of computer software and hardware, deep-learning techniques have been widely applied in image processing [8], such as computational imaging [9], image segmentation [10], and super-resolution reconstruction [11]. Researchers have recently integrated deep-learning techniques into digital holography to address the problems encountered in traditional holographic reconstruction [12, 13, 14, 15, 16]. For example, [PERSON] et al. proposed using neural networks to achieve end-to-end coaxial digital holographic reconstruction [17]. [PERSON] et al. used trained neural networks to eliminate the twin image artifacts in coaxial holographic reconstruction [18]. [PERSON] et al. combined U-Net and Ozcan networks to propose eHoloNet, which can directly reconstruct holograms without considering the zero-order component [19]. [PERSON] et al. introduced Y-Net, a Y-shaped convolutional neural network, for off-axis hologram reconstruction. This network can simultaneously reconstruct intensity and phase information from a single input digitalhologram [20]. The above-mentioned networks proposed by the researchers are based on a single-scale architecture, which is only able to handle input and output on a single scale. The acquisition of digital holograms requires a precise design of the experimental setup. The resulting holograms may have high-frequency information concentrated in one area and low-frequency information scattered around it, resulting in large hologram sizes and slow reconstruction speeds. Cropping only the high-frequency information for reconstruction may result in the loss of useful information from the surrounding regions, leading to lower reconstruction quality.  
To address the above issues, we propose an enhanced deep-learning model called Mimo-Net. The model extracts locally important features from large-scale holograms in a small-scale form. It serially constructs three input branches within a single network structure, from global to local, to feed into the leading network. Mimo-Net enlarges the receptive field and obtains the global information and detail information under different receptive fields. It also uses a multiscale feature-extraction module to enhance feature extraction and improve reconstruction accuracy. Finally, the multiscale features can be input into a single down-sampling path. At the same time, the up-sampling path can simultaneously output intensity information and phase information of multiple scales in a dual-channel format.