HoloForkNet: Digital Hologram Reconstruction via Multibranch Neural Network

Abstract

Reconstruction of 3D scenes from digital holograms is an important task in different areas of science, such as biology, medicine, ecology, etc. A lot of parameters, such as the object’s shape, number, position, rate and density, can be extracted. However, reconstruction of off-axis and especially inline holograms can be challenging due to the presence of optical noise, zero-order image and twin image. We have used a deep-multibranch neural network model, which we call HoloForkNet, to reconstruct different 2D sections of a 3D scene from a single inline hologram. This paper describes the proposed method and analyzes its performance for different types of objects. Both computer-generated and optically registered digital holograms with resolutions up to 2048 × 2048 pixels were reconstructed. High-quality image reconstruction for scenes consisting of up to eight planes was achieved. The average structural similarity index (SSIM) for 3D test scenes with eight object planes was 0.94. The HoloForkNet can be used to reconstruct 3D scenes consisting of micro- and macro-objects.

Generative adversarial neural network for 3D-hologram reconstruction

“…The generator is a CNN, based on the architecture U-Net [43,56], the discriminator is a CNN with 10 convolutional layers. The generator and discriminator architectures are shown in figures 2 and 3.…”

“…The training time of the GAN can be shortened decreasing the resolution of the images, for example by using techniques such as pixel interleaving [63]. Additionally, the performance of the GAN can be improved by optimizing the loss functions, and the generator and discriminator architectures, for example, by adding branching [43].…”

“…CNNs are typically used to reconstruct one image [24][25][26][27][28][29][30][31][32] or two (amplitude and phase information) [33][34][35][36][37][38][39][40][41] or extended focus imaging [41,42]. However, the direct reconstruction of the entire 3D-scene provides a wider range of possibilities [43].…”

Deep-Learning Multiscale Digital Holographic Intensity and Phase Reconstruction

“…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, Sinha et al proposed using neural networks to achieve end-to-end coaxial digital holographic reconstruction [17].…”