Efficient Depth- and Spatially-Varying Image Simulation for Defocus Deblur

• URL: http://arxiv.org/abs/2507.00372v1
• Date: Tue, 01 Jul 2025 02:03:04 GMT
• Title: Efficient Depth- and Spatially-Varying Image Simulation for Defocus Deblur
• Authors: Xinge Yang, Chuong Nguyen, Wenbin Wang, Kaizhang Kang, Wolfgang Heidrich, Xiaoxing Li,
• Abstract summary: Deep learning models trained on existing open-source datasets often face domain gaps and do not perform well in real-world settings.<n>We propose an efficient and scalable dataset approach that does not rely on fine-tuning with real-world data.<n>Our method simultaneously models depth-dependent defocus and spatially varying optical aberrations, addressing both computational complexity and the scarcity of high-quality RGB-D datasets.
• Score: 16.9629875455607
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Modern cameras with large apertures often suffer from a shallow depth of field, resulting in blurry images of objects outside the focal plane. This limitation is particularly problematic for fixed-focus cameras, such as those used in smart glasses, where adding autofocus mechanisms is challenging due to form factor and power constraints. Due to unmatched optical aberrations and defocus properties unique to each camera system, deep learning models trained on existing open-source datasets often face domain gaps and do not perform well in real-world settings. In this paper, we propose an efficient and scalable dataset synthesis approach that does not rely on fine-tuning with real-world data. Our method simultaneously models depth-dependent defocus and spatially varying optical aberrations, addressing both computational complexity and the scarcity of high-quality RGB-D datasets. Experimental results demonstrate that a network trained on our low resolution synthetic images generalizes effectively to high resolution (12MP) real-world images across diverse scenes.

Related papers

• Exposure Bracketing Is All You Need For A High-Quality Image [50.822601495422916]
  Multi-exposure images are complementary in denoising, deblurring, high dynamic range imaging, and super-resolution.<n>We propose to utilize exposure bracketing photography to get a high-quality image by combining these tasks in this work.<n>In particular, a temporally modulated recurrent network (TMRNet) and self-supervised adaptation method are proposed.
  arXiv  Detail & Related papers  (2024-01-01T14:14:35Z)
• Towards Real-World Focus Stacking with Deep Learning [97.34754533628322]
  We introduce a new dataset consisting of 94 high-resolution bursts of raw images with focus bracketing. This dataset is used to train the first deep learning algorithm for focus stacking capable of handling bursts of sufficient length for real-world applications.
  arXiv  Detail & Related papers  (2023-11-29T17:49:33Z)
• Improving Lens Flare Removal with General Purpose Pipeline and Multiple Light Sources Recovery [69.71080926778413]
  flare artifacts can affect image visual quality and downstream computer vision tasks. Current methods do not consider automatic exposure and tone mapping in image signal processing pipeline. We propose a solution to improve the performance of lens flare removal by revisiting the ISP and design a more reliable light sources recovery strategy.
  arXiv  Detail & Related papers  (2023-08-31T04:58:17Z)
• Aberration-Aware Depth-from-Focus [20.956132508261664]
  We investigate the domain gap caused by off-axis aberrations that will affect the decision of the best-focused frame in a focal stack. We then explore bridging this domain gap through aberration-aware training (AAT) Our approach involves a lightweight network that models lens aberrations at different positions and focus distances, which is then integrated into the conventional network training pipeline.
  arXiv  Detail & Related papers  (2023-03-08T15:21:33Z)
• High Dynamic Range and Super-Resolution from Raw Image Bursts [52.341483902624006]
  This paper introduces the first approach to reconstruct high-resolution, high-dynamic range color images from raw photographic bursts captured by a handheld camera with exposure bracketing. The proposed algorithm is fast, with low memory requirements compared to state-of-the-art learning-based approaches to image restoration. Experiments demonstrate its excellent performance with super-resolution factors of up to $times 4$ on real photographs taken in the wild with hand-held cameras.
  arXiv  Detail & Related papers  (2022-07-29T13:31:28Z)
• Learning to Deblur using Light Field Generated and Real Defocus Images [4.926805108788465]
  Defocus deblurring is a challenging task due to the spatially varying nature of defocus blur. We propose a novel deep defocus deblurring network that leverages the strength and overcomes the shortcoming of light fields.
  arXiv  Detail & Related papers  (2022-04-01T11:35:51Z)
• MC-Blur: A Comprehensive Benchmark for Image Deblurring [127.6301230023318]
  In most real-world images, blur is caused by different factors, e.g., motion and defocus. We construct a new large-scale multi-cause image deblurring dataset (called MC-Blur) Based on the MC-Blur dataset, we conduct extensive benchmarking studies to compare SOTA methods in different scenarios.
  arXiv  Detail & Related papers  (2021-12-01T02:10:42Z)
• Decentralized Autofocusing System with Hierarchical Agents [2.7716102039510564]
  We propose a hierarchical multi-agent deep reinforcement learning approach for intelligently controlling the camera and the lens focusing settings. The algorithm relies on the latent representation of the camera's stream and, thus, it is the first method to allow a completely no-reference tuning of the camera.
  arXiv  Detail & Related papers  (2021-08-29T13:45:15Z)
• Universal and Flexible Optical Aberration Correction Using Deep-Prior Based Deconvolution [51.274657266928315]
  We propose a PSF aware plug-and-play deep network, which takes the aberrant image and PSF map as input and produces the latent high quality version via incorporating lens-specific deep priors. Specifically, we pre-train a base model from a set of diverse lenses and then adapt it to a given lens by quickly refining the parameters.
  arXiv  Detail & Related papers  (2021-04-07T12:00:38Z)
• Exploiting Raw Images for Real-Scene Super-Resolution [105.18021110372133]
  We study the problem of real-scene single image super-resolution to bridge the gap between synthetic data and real captured images. We propose a method to generate more realistic training data by mimicking the imaging process of digital cameras. We also develop a two-branch convolutional neural network to exploit the radiance information originally-recorded in raw images.
  arXiv  Detail & Related papers  (2021-02-02T16:10:15Z)

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.