Reducing crosstalk of a multi-plane holographic display by the time-multiplexing stochastic gradient descent

Solution to the issue of high-order diffraction images for cylindrical computer-generated holograms

The cylindrical computer-generated hologram (CCGH), featuring a 360° viewing zone, has garnered widespread attention. However, the issue of high-order diffraction images due to pixelated structure in CCGH has not been previously reported and solved. For a cylindrical model offering a 360° viewing zone in the horizontal direction, the high-order diffraction images always overlap with the reconstruction image, leading to quality degradation. Furthermore, the 4f system is commonly used to eliminate high-order diffraction images in planar CGH, but its implementation is predictably complex for a cylindrical model. In this paper, we propose a solution to the issue of high-order diffraction images for CCGH. We derive the cylindrical diffraction formula from the outer hologram surface to the inner object surface in the spectral domain, and based on this, we subsequently analyze the effects brought by the pixel structure and propose the high-order diffraction model. Based on the proposed high-order diffraction model, we use the gradient descent method to optimize CCGH accounting for all diffraction orders simultaneously. Furthermore, we discuss the issue of circular convolution due to the periodicity of the Fast Fourier Transform (FFT) in cylindrical diffraction. The correctness of the proposed high-order diffraction model and the effectiveness of the proposed optimization method are demonstrated by numerical simulation. To our knowledge, this is the first time that the issue of high-order diffraction images in CCGH has been proposed, and we believe our solution can offer valuable guidance to practitioners in the field.

High quality holographic 3D display with enhanced focus cues based on multiple directional light reconstruction

Holographic display faces the trade-off between image quality and focus cues, resulting from the specific choice of phase distribution. In this Letter, we propose a speckle-free holographic display with enhanced focus cues by multiple directional light reconstruction. The uniform phase hologram is first generated by the gradient descent optimization algorithm. The blazed grating phase is used to steer the object light to a specific direction. Multiple sub-holograms with different blazed gratings are refreshed fast to reconstruct the images from different directions. Thus, the defocus blur is improved due to the separation of multiple spots on the defocus plane. The multi-plane reconstruction is also realized by pre-processing the depth images to eliminate image separation. The proposed method provides apparent focus cues while maintaining high image qualities, which is expected to realize comfortable holographic near-eye display in the near future.

Deep learning-assisted light sheet holography

In a novel approach to layer-based holography, we propose a machine learning-assisted light sheet holography-an optimized holography technique which projects a target scene onto sheets of light along the longitudinal planes (i.e. planes perpendicular to the plane of the hologram). Using a convolutional neural network in conjunction with superposition of Bessel beams, we generate high-definition images which can be stacked in parallel onto longitudinal planes with very high fidelity. Our holography system provides high axial resolution and excellent control over the light intensity along the optical path, which is suitable for augmented reality and/or virtual reality applications.

Three-dimensional computer-generated holography based on the hybrid iterative angular spectrum algorithm

This paper proposes a novel three-dimensional hologram calculation method based on the angular spectrum approach, with the aim of reducing the noise generated during the hologram reconstruction process. The proposed algorithm divides the spatial domain into multiple layers and employs the angular spectrum method to propagate the image between these layers, thus avoiding the paraxial approximation. To enhance the quality of the hologram, an error iteration algorithm is utilized to alleviate the occurrence of aliasing errors when directly superimposing holograms. Moreover, constraint factors are introduced between different layers within the same region to effectively utilize spatial resources for multi-image reconstruction, thereby mitigating the noise caused by inter-layer crosstalk. The feasibility of the proposed method is demonstrated through numerical simulations and optical experiments, highlighting its potential applicability to a wide range of three-dimensional reconstruction algorithms.

Research on improving holographic display quality based on phase compensation of reproduced image light energy distribution

Liquid crystal spatial light modulator (LC-SLM) holographic display is affected by its structure, which products multi-level diffracted image with zero-order spot, resulting in low light energy utilization and poor uniformity of the reproduced image. This paper presents a method to improve the uniformity of light energy distribution in the reproduced image by using phase compensation, and the uniformity of the image can be effectively improved by using digital blazed grating to deviate the image and performing phase compensation according to the light energy distribution. Analyzing the uniformity of light energy distribution, the phase distribution is compensated, and experiments verify the phase compensation. The experimental results show that the uniformity and light energy utilization of the reproduced image after compensation has been improved. The results show that the proposed phase compensation method can be applied to both Fresnel holography and Fourier holography; both can effectively improve the uniformity and efficiency of light energy. Therefore, this method has a specific application value to enhance the quality of holographic reproduction and light field modulation based on LC-SLM.

DCPNet: a dual-channel parallel deep neural network for high quality computer-generated holography

Recent studies have demonstrated that a learning-based computer-generated hologram (CGH) has great potential for real-time, high-quality holographic displays. However, most existing algorithms treat the complex-valued wave field as a two-channel spatial domain image to facilitate mapping onto real-valued kernels, which does not fully consider the computational characteristics of complex amplitude. To address this issue, we proposed a dual-channel parallel neural network (DCPNet) for generating phase-only holograms (POHs), taking inspiration from the double phase amplitude encoding method. Instead of encoding the complex-valued wave field in the SLM plane as a two-channel image, we encode it into two real-valued phase elements. Then the two learned sub-POHs are sampled by the complementary 2D binary grating to synthesize the desired POH. Simulation and optical experiments are carried out to verify the feasibility and effectiveness of the proposed method. The simulation results indicate that the DCPNet is capable of generating high-fidelity 2k POHs in 36 ms. The optical experiments reveal that the DCPNet has excellent ability to preserve finer details, suppress speckle noise and improve uniformity in the reconstructed images.

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.

Diffraction model-driven neural network trained using hybrid domain loss for real-time and high-quality computer-generated holography

Learning-based computer-generated holography (CGH) has demonstrated great potential in enabling real-time, high-quality holographic displays. However, most existing learning-based algorithms still struggle to produce high-quality holograms, due to the difficulty of convolutional neural networks (CNNs) in learning cross-domain tasks. Here, we present a diffraction model-driven neural network (Res-Holo) using hybrid domain loss for phase-only hologram (POH) generation. Res-Holo utilizes the weights of the pretrained ResNet34 as the initialization during the encoder stage of the initial phase prediction network to extract more generic features and also to help prevent overfitting. Also, frequency domain loss is added to further constrain the information that the spatial domain loss is insensitive. The peak signal-to-noise ratio (PSNR) of the reconstructed image is improved by 6.05 dB using hybrid domain loss compared to using spatial domain loss alone. Simulation results show that the proposed Res-Holo can generate high-fidelity 2 K resolution POHs with an average PSNR of 32.88 dB at 0.014 seconds/frame on the DIV2K validation set. Both monochrome and full-color optical experiments show that the proposed method can effectively improve the quality of reproduced images and suppress image artifacts.