Defocus Aberration Theory Confirms Gaussian Model in Most Imaging Devices

• URL: http://arxiv.org/abs/2601.04779v1
• Date: Thu, 08 Jan 2026 10:03:13 GMT
• Title: Defocus Aberration Theory Confirms Gaussian Model in Most Imaging Devices
• Authors: Akbar Saadat,
• Abstract summary: This paper introduces the settings, for conventional imaging devices, to ensure that the defocusing operator adheres to the Gaussian model.<n>The results demonstrate a maximum Mean Absolute Error $(!M!A!E)$ of less than $1%$, underscoring the model's accuracy and reliability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Over the past three decades, defocus has consistently provided groundbreaking depth information in scene images. However, accurately estimating depth from 2D images continues to be a persistent and fundamental challenge in the field of 3D recovery. Heuristic approaches involve with the ill-posed problem for inferring the spatial variant defocusing blur, as the desired blur cannot be distinguished from the inherent blur. Given a prior knowledge of the defocus model, the problem become well-posed with an analytic solution for the relative blur between two images, taken at the same viewpoint with different camera settings for the focus. The Gaussian model stands out as an optimal choice for real-time applications, due to its mathematical simplicity and computational efficiency. And theoretically, it is the only model can be applied at the same time to both the absolute blur caused by depth in a single image and the relative blur resulting from depth differences between two images. This paper introduces the settings, for conventional imaging devices, to ensure that the defocusing operator adheres to the Gaussian model. Defocus analysis begins within the framework of geometric optics and is conducted by defocus aberration theory in diffraction-limited optics to obtain the accuracy of fitting the actual model to its Gaussian approximation. The results for a typical set of focused depths between $1$ and $100$ meters, with a maximum depth variation of $10\%$ at the focused depth, confirm the Gaussian model's applicability for defocus operators in most imaging devices. The findings demonstrate a maximum Mean Absolute Error $(\!M\!A\!E)$ of less than $1\%$, underscoring the model's accuracy and reliability.

Related papers

• BokehDiff: Neural Lens Blur with One-Step Diffusion [62.59018200914645]
  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)
• Depth Estimation Based on 3D Gaussian Splatting Siamese Defocus [14.354405484663285]
  We propose a self-supervised framework based on 3D Gaussian splatting and Siamese networks for depth estimation in 3D geometry.<n>The proposed framework has been validated on both artificially synthesized and real blurred datasets.
  arXiv  Detail & Related papers  (2024-09-18T21:36:37Z)
• Blur aware metric depth estimation with multi-focus plenoptic cameras [8.508198765617196]
  We present a new metric depth estimation algorithm using only raw images from a multi-focus plenoptic camera. The proposed approach is especially suited for the multi-focus configuration where several micro-lenses with different focal lengths are used.
  arXiv  Detail & Related papers  (2023-08-08T13:38:50Z)
• Single Image Depth Prediction Made Better: A Multivariate Gaussian Take [163.14849753700682]
  We introduce an approach that performs continuous modeling of per-pixel depth. Our method's accuracy (named MG) is among the top on the KITTI depth-prediction benchmark leaderboard.
  arXiv  Detail & Related papers  (2023-03-31T16:01:03Z)
• Fully Self-Supervised Depth Estimation from Defocus Clue [79.63579768496159]
  We propose a self-supervised framework that estimates depth purely from a sparse focal stack. We show that our framework circumvents the needs for the depth and AIF image ground-truth, and receives superior predictions.
  arXiv  Detail & Related papers  (2023-03-19T19:59:48Z)
• Monocular 3D Object Detection with Depth from Motion [74.29588921594853]
  We take advantage of camera ego-motion for accurate object depth estimation and detection. Our framework, named Depth from Motion (DfM), then uses the established geometry to lift 2D image features to the 3D space and detects 3D objects thereon. Our framework outperforms state-of-the-art methods by a large margin on the KITTI benchmark.
  arXiv  Detail & Related papers  (2022-07-26T15:48:46Z)
• End-to-end Learning for Joint Depth and Image Reconstruction from Diffracted Rotation [10.896567381206715]
  We propose a novel end-to-end learning approach for depth from diffracted rotation. Our approach requires a significantly less complex model and less training data, yet it is superior to existing methods in the task of monocular depth estimation.
  arXiv  Detail & Related papers  (2022-04-14T16:14:37Z)
• Precise Point Spread Function Estimation [6.076995573805468]
  We develop a precise mathematical model of the camera's point spread function to describe the defocus process. Our experiments on standard planes and actual objects show that the proposed algorithm can accurately describe the defocus process.
  arXiv  Detail & Related papers  (2022-03-06T12:43:27Z)
• Single image deep defocus estimation and its applications [82.93345261434943]
  We train a deep neural network to classify image patches into one of the 20 levels of blurriness. The trained model is used to determine the patch blurriness which is then refined by applying an iterative weighted guided filter. The result is a defocus map that carries the information of the degree of blurriness for each pixel.
  arXiv  Detail & Related papers  (2021-07-30T06:18:16Z)
• A learning-based view extrapolation method for axial super-resolution [52.748944517480155]
  Axial light field resolution refers to the ability to distinguish features at different depths by refocusing. We propose a learning-based method to extrapolate novel views from axial volumes of sheared epipolar plane images.
  arXiv  Detail & Related papers  (2021-03-11T07:22:13Z)

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.