Wavefront Coding for Accommodation-Invariant Near-Eye Displays

• URL: http://arxiv.org/abs/2510.12778v1
• Date: Tue, 14 Oct 2025 17:52:28 GMT
• Title: Wavefront Coding for Accommodation-Invariant Near-Eye Displays
• Authors: Ugur Akpinar, Erdem Sahin, Tina M. Hayward, Apratim Majumder, Rajesh Menon, Atanas Gotchev,
• Abstract summary: We present a new computational near-eye display method that addresses the vergence-accommodation conflict problem in stereoscopic displays.<n>Our system integrates a refractive lens eyepiece with a novel wavefront coding diffractive optical element.<n>We employ end-to-end learning to jointly optimize the wavefront-coding optics and the image pre-processing module.
• Score: 1.7240671897505615
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present a new computational near-eye display method that addresses the vergence-accommodation conflict problem in stereoscopic displays through accommodation-invariance. Our system integrates a refractive lens eyepiece with a novel wavefront coding diffractive optical element, operating in tandem with a pre-processing convolutional neural network. We employ end-to-end learning to jointly optimize the wavefront-coding optics and the image pre-processing module. To implement this approach, we develop a differentiable retinal image formation model that accounts for limiting aperture and chromatic aberrations introduced by the eye optics. We further integrate the neural transfer function and the contrast sensitivity function into the loss model to account for related perceptual effects. To tackle off-axis distortions, we incorporate position dependency into the pre-processing module. In addition to conducting rigorous analysis based on simulations, we also fabricate the designed diffractive optical element and build a benchtop setup, demonstrating accommodation-invariance for depth ranges of up to four diopters.

Related papers

• BokehDiff: Neural Lens Blur with One-Step Diffusion [53.11429878683807]
  We introduce BokehDiff, a lens blur rendering method that achieves physically accurate and visually appealing outcomes.<n>Our method employs a physics-inspired self-attention module that aligns with the image formation process.<n>We adapt the diffusion model to the one-step inference scheme without introducing additional noise, and achieve results of high quality and fidelity.
  arXiv  Detail & Related papers  (2025-07-24T03:23:19Z)
• Fovea Stacking: Imaging with Dynamic Localized Aberration Correction [10.382234835644327]
  Fovea Stacking is a new type of imaging system that utilizes dynamic optical components called deformable phase plates (DPPs) for localized aberration correction.<n>By optimizing DPP deformations through a differentiable optical model, off-axis aberrations are corrected locally, producing a foveated image with enhanced sharpness at the fixation point - analogous to the eye's fovea.
  arXiv  Detail & Related papers  (2025-05-31T21:15:27Z)
• Nonlocal Retinex-Based Variational Model and its Deep Unfolding Twin for Low-Light Image Enhancement [3.174882428337821]
  We propose a variational method for low-light image enhancement based on the Retinex decomposition.<n>A color correction pre-processing step is applied to the low-light image, which is then used as the observed input in the decomposition.<n>We extend the model by introducing its deep unfolding counterpart, in which the operators are replaced with learnable networks.
  arXiv  Detail & Related papers  (2025-04-10T14:48:26Z)
• Successive optimization of optics and post-processing with differentiable coherent PSF operator and field information [9.527960631238173]
  We introduce a precise optical simulation model, and every operation in pipeline is differentiable.<n>To efficiently address various degradation, we design a joint optimization procedure that leverages field information.
  arXiv  Detail & Related papers  (2024-12-19T07:49:40Z)
• Oscillation Inversion: Understand the structure of Large Flow Model through the Lens of Inversion Method [60.88467353578118]
  We show that a fixed-point-inspired iterative approach to invert real-world images does not achieve convergence, instead oscillating between distinct clusters. We introduce a simple and fast distribution transfer technique that facilitates image enhancement, stroke-based recoloring, as well as visual prompt-guided image editing.
  arXiv  Detail & Related papers  (2024-11-17T17:45:37Z)
• Learning Robust Multi-Scale Representation for Neural Radiance Fields from Unposed Images [65.41966114373373]
  We present an improved solution to the neural image-based rendering problem in computer vision. The proposed approach could synthesize a realistic image of the scene from a novel viewpoint at test time.
  arXiv  Detail & Related papers  (2023-11-08T08:18:23Z)
• Revealing the preference for correcting separated aberrations in joint optic-image design [19.852225245159598]
  We characterize the optics with separated aberrations to achieve efficient joint design of complex systems such as smartphones and drones. An image simulation system is presented to reproduce the genuine imaging procedure of lenses with large field-of-views. Experiments reveal that the preference for correcting separated aberrations in joint design is as follows: longitudinal chromatic aberration, lateral chromatic aberration, spherical aberration, field curvature, and coma, with astigmatism coming last.
  arXiv  Detail & Related papers  (2023-09-08T14:12:03Z)
• Optical Flow Estimation from a Single Motion-blurred Image [66.2061278123057]
  Motion blur in an image may have practical interests in fundamental computer vision problems. We propose a novel framework to estimate optical flow from a single motion-blurred image in an end-to-end manner.
  arXiv  Detail & Related papers  (2021-03-04T12:45:18Z)
• Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces [103.08512487830669]
  This paper presents an uncalibrated deep neural network framework for the photometric stereo problem. Existing neural network-based methods either require exact light directions or ground-truth surface normals of the object or both. We propose an uncalibrated neural inverse rendering approach to this problem.
  arXiv  Detail & Related papers  (2020-12-12T10:33:08Z)
• SIR: Self-supervised Image Rectification via Seeing the Same Scene from Multiple Different Lenses [82.56853587380168]
  We propose a novel self-supervised image rectification (SIR) method based on an important insight that the rectified results of distorted images of the same scene from different lens should be the same. We leverage a differentiable warping module to generate the rectified images and re-distorted images from the distortion parameters. Our method achieves comparable or even better performance than the supervised baseline method and representative state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-11-30T08:23:25Z)
• Learning to Model and Calibrate Optics via a Differentiable Wave Optics Simulator [27.913052825303097]
  We present a novel learning-based method to build a differentiable computational model of a real fluorescence microscope. Our model can be used to calibrate a real optical setup directly from data samples and to engineer point spread functions by specifying the desired input-output data.
  arXiv  Detail & Related papers  (2020-05-18T10:23:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.