Depth of field and resolution-enhanced integral imaging display system

Display performance optimization method for light field displays based on a neural network

Crosstalk between adjacent views, lens aberrations, and low spatial resolution in light field displays limit the quality of 3D images. In the present study, we introduce a display performance optimization method for light field displays based on a neural network. The method pre-corrects the encoded image from a global perspective, which means that the encoded image is pre-corrected according to the light field display results. The display performance optimization network consists of two parts: the encoded image pre-correction network and the display network. The former realizes the pre-correction of the original encoded image (OEI), while the latter completes the modeling of the display unit and realizes the generation from the encoded image to the viewpoint images (VIs). The pre-corrected encoded image (PEI) obtained through the pre-correction network can reconstruct 3D images with higher quality. The VIs are accessible through the display network. Experimental results suggest that the proposed method can reduce the graininess of 3D images significantly without increasing the complexity of the system. It is promising for light field displays since it can provide improved 3D display performance.

Mapping-based design method for high-quality integral projection system

A general method for designing an integral projection system is proposed, including optical design and digital preprocessing based on the mapping within the projection system. The per-pixel mapping between the sub-images and the integral projection image is generated by incorporating an integral projection imaging model as well as the ray data of all sub-channels. By tracing rays for sparsely sampled field points of the central sub-channel and constructing the mapping between the central sub-channel and other sub-channels, the efficient acquisition of ray data for all sub-channels is achieved. The sub-image preprocessing pipeline is presented to effectively address issues such as overlapping misalignment, optical aberrations, inhomogeneous illumination, and their collective contribution. An integral projection optical system with a field of view (FOV) of 80°, an F-number of 2, and uniform image performance is given as a design example. The ray tracing simulation results and quantitative analysis demonstrate that the proposed system yields distortion-free, uniformly illuminated, and high-quality integral projection images.

Depth of field expansion method for integral imaging based on diffractive optical element and CNN

In lens-based display systems, lens aberrations and depth of field (DoF) limitation often lead to blurring and distortion of reconstructed images; Meanwhile, expanding the display DoF will face a trade-off between horizontal resolution and axial resolution, restricting the achievement of high-resolution and large DoF three-dimensional (3D) displays. To overcome these constraints and enhance the DoF and resolution of reconstructed scenes, we propose a DoF expansion method based on diffractive optical element (DOE) optimization and image pre-correction through a convolutional neural network (CNN). This method applies DOE instead of the conventional lens and optimizes DOE phase distribution using the Adam algorithm, achieving depth-invariant and concentrated point spread function (PSF) distribution throughout the entire DoF range; Simultaneously, we utilize a CNN to pre-correct the original images and compensate for the image quality reduction introduced by the DOE. The proposed method is applied to a practical integral imaging system, we effectively extend the DoF of the DOE to 400 mm, leading to a high-resolution 3D display in multiple depth planes. To validate the effectiveness and practicality of the proposed method, we conduct numerical simulations and optical experiments.

Real-time computer-generated integral imaging light field displays: revisiting the point retracing rendering method from a signal processing perspective

Integral imaging light field displays (InIm-LFDs) can provide realistic 3D images by showing an elemental image array (EIA) under a lens array. However, it is always challenging to computationally generate an EIA in real-time with entry-level computing hardware because the current practice that projects many viewpoints to the EIA induces heavy computations. This study discards the viewpoint-based strategy, revisits the early point retracing rendering method, and proposes that InIm-LFDs and regular 2D displays share two similar signal processing phases: sampling and reconstructing. An InIm-LFD is demonstrated to create a finite number of static voxels for signal sampling. Each voxel is invariantly formed by homogeneous pixels for signal reconstructing. We obtain the static voxel-pixel mapping through arbitrarily accurate raytracing in advance and store it as a lookup table (LUT). Our EIA rendering method first resamples input 3D data with the pre-defined voxels and then assigns every voxel's value to its homogeneous pixels through the LUT. As a result, the proposed method reduces the computational complexity by several orders of magnitude. The experimental rendering speed is as fast as 7 to 10 ms for a full-HD EIA frame on an entry-level laptop. Finally, considering a voxel may not be perfectly integrated by its homogeneous pixels, called the sampling error, the proposed and conventional viewpoint-based methods are analyzed in the Fourier domain. We prove that even with severe sampling errors, the two methods negligibly differ in the output signal's frequency spectrum. We expect the proposed method to break the long-standing tradeoff between rendering speed, accuracy, and system complexity for computer-generated integral imaging.

Introduction to the feature issue on augmented/virtual reality: optics & photonics

In recent years, augmented/virtual reality (AR/VR) has been attracting attention and investment in both the tech and academic communities, kickstarting a new wave of innovations. In the wake of this momentum, this feature issue was launched to cover the latest advances in this burgeoning field that pertains to optics and photonics. Alongside the 31 research articles being published, this introduction is appended to share with readers the behind-the-issue stories, submission statistics, reading guides, author biographies, and editors' perspectives.