Improved phase hologram generation of multiple 3D objects

Non-convex optimization for inverse problem solving in computer-generated holography

Computer-generated holography is a promising technique that modulates user-defined wavefronts with digital holograms. Computing appropriate holograms with faithful reconstructions is not only a problem closely related to the fundamental basis of holography but also a long-standing challenge for researchers in general fields of optics. Finding the exact solution of a desired hologram to reconstruct an accurate target object constitutes an ill-posed inverse problem. The general practice of single-diffraction computation for synthesizing holograms can only provide an approximate answer, which is subject to limitations in numerical implementation. Various non-convex optimization algorithms are thus designed to seek an optimal solution by introducing different constraints, frameworks, and initializations. Herein, we overview the optimization algorithms applied to computer-generated holography, incorporating principles of hologram synthesis based on alternative projections and gradient descent methods. This is aimed to provide an underlying basis for optimized hologram generation, as well as insights into the cutting-edge developments of this rapidly evolving field for potential applications in virtual reality, augmented reality, head-up display, data encryption, laser fabrication, and metasurface design.

Vision transformer empowered physics-driven deep learning for omnidirectional three-dimensional holography

The inter-plane crosstalk and limited axial resolution are two key points that hinder the performance of three-dimensional (3D) holograms. The state-of-the-art methods rely on increasing the orthogonality of the cross-sections of a 3D object at different depths to lower the impact of inter-plane crosstalk. Such strategy either produces unidirectional 3D hologram or induces speckle noise. Recently, learning-based methods provide a new way to solve this problem. However, most related works rely on convolution neural networks and the reconstructed 3D holograms have limited axial resolution and display quality. In this work, we propose a vision transformer (ViT) empowered physics-driven deep neural network which can realize the generation of omnidirectional 3D holograms. Owing to the global attention mechanism of ViT, our 3D CGH has small inter-plane crosstalk and high axial resolution. We believe our work not only promotes high-quality 3D holographic display, but also opens a new avenue for complex inverse design in photonics.

Crosstalk-free for multi-plane holographic display using double-constraint stochastic gradient descent

Multi-plane crosstalk is a key issue affecting the quality of holographic three-dimensional (3D) displays. The time-multiplexing stochastic gradient descent (TM-SGD) method has been applied to solve the inter-plane crosstalk problem in multi-plane reconstruction. However, the inter-plane crosstalk increases greatly as the inter-plane interval decreases, and the optimization time increases greatly as the number of planes increases. In this paper, we propose a double-constraint stochastic gradient descent method to suppress inter-plane crosstalk in multi-plane reconstruction. In the proposed method, we use the mask to make the optimization process focus more on the signal region and improve the reconstruction quality. Meanwhile, we adopt a constraint strategy of phase regularization to reduce the phase randomness of the signal region and suppress inter-plane crosstalk. Numerical simulation and optical experiment results confirm that our method can effectively suppress the inter-plane crosstalk and improve the quality of the reconstructed planes at a lower inter-plane interval. Moreover, the optimization time of our method is almost 4 times faster than that of TM-SGD. The proposed method can contribute to the realization of tomographic 3D visualization in the biomedical field, which requires the reconstruction of multiple tomographic images without inter-plane crosstalk.

Color multilayer holographic near-eye augmented reality display

This study demonstrates a full-color near-eye holographic display capable of superimposing color virtual scenes with 2D, 3D, and multiple objects with extended depth upon a real scene, which also has the ability to present different 3D information depending on the focus of the user's eyes using a single computer-generated hologram per color channel. Our setup makes use of a hologram generation method based on two-step propagation and the singular value decomposition of the Fresnel transform impulse response function to efficiently generate the holograms of the target scene. Then, we test our proposal by implementing a holographic display that makes use of a phase-only spatial light modulator and time-division multiplexing for color reproduction. We demonstrate the superior quality and computation speed of this approach compared with other hologram generation techniques with both numerical and experimental results.

Diffraction-engineered holography: Beyond the depth representation limit of holographic displays

Holography is one of the most prominent approaches to realize true-to-life reconstructions of objects. However, owing to the limited resolution of spatial light modulators compared to static holograms, reconstructed objects exhibit various coherent properties, such as content-dependent defocus blur and interference-induced noise. The coherent properties severely distort depth perception, the core of holographic displays to realize 3D scenes beyond 2D displays. Here, we propose a hologram that imitates defocus blur of incoherent light by engineering diffracted pattern of coherent light with adopting multi-plane holography, thereby offering real world-like defocus blur and photorealistic reconstruction. The proposed hologram is synthesized by optimizing a wave field to reconstruct numerous varifocal images after propagating the corresponding focal distances where the varifocal images are rendered using a physically-based renderer. Moreover, to reduce the computational costs associated with rendering and optimizing, we also demonstrate a network-based synthetic method that requires only an RGB-D image.