OmniLens++: Blind Lens Aberration Correction via Large LensLib Pre-Training and Latent PSF Representation

• URL: http://arxiv.org/abs/2511.17126v3
• Date: Tue, 25 Nov 2025 11:06:54 GMT
• Title: OmniLens++: Blind Lens Aberration Correction via Large LensLib Pre-Training and Latent PSF Representation
• Authors: Qi Jiang, Xiaolong Qian, Yao Gao, Lei Sun, Kailun Yang, Zhonghua Yi, Wenyong Li, Ming-Hsuan Yang, Luc Van Gool, Kaiwei Wang,
• Abstract summary: This work proposes the OmniLens++ framework, which resolves two challenges that hinder the generalization ability of existing pipelines.<n>Experiments on diverse aberrations of real-world lenses and synthetic LensLib show that OmniLens++ exhibits state-of-the-art generalization capacity in blind aberration correction.
• Score: 72.72583225885636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emerging deep-learning-based lens library pre-training (LensLib-PT) pipeline offers a new avenue for blind lens aberration correction by training a universal neural network, demonstrating strong capability in handling diverse unknown optical degradations. This work proposes the OmniLens++ framework, which resolves two challenges that hinder the generalization ability of existing pipelines: the difficulty of scaling data and the absence of prior guidance characterizing optical degradation. To improve data scalability, we expand the design specifications to increase the degradation diversity of the lens source, and we sample a more uniform distribution by quantifying the spatial-variation patterns and severity of optical degradation. In terms of model design, to leverage the Point Spread Functions (PSFs), which intuitively describe optical degradation, as guidance in a blind paradigm, we propose the Latent PSF Representation (LPR). The VQVAE framework is introduced to learn latent features of LensLib's PSFs, which is assisted by modeling the optical degradation process to constrain the learning of degradation priors. Experiments on diverse aberrations of real-world lenses and synthetic LensLib show that OmniLens++ exhibits state-of-the-art generalization capacity in blind aberration correction. Beyond performance, the AODLibpro is verified as a scalable foundation for more effective training across diverse aberrations, and LPR can further tap the potential of large-scale LensLib. The source code and datasets will be made publicly available at https://github.com/zju-jiangqi/OmniLens2.

Related papers

• Learning to Remove Lens Flare in Event Camera [56.9171469873838]
  We present E-DeflareDeflare, the first framework for removing lens flare from event camera data.<n>We first establish the theoretical foundation by deriving a physics-grounded forward model of the non-linear suppression mechanism.<n> Empowered by this benchmark, we design E-DeflareNet, which achieves state-of-the-art restoration performance.
  arXiv  Detail & Related papers  (2025-12-09T18:59:57Z)
• LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction [32.85180149439811]
  Lensless imaging stands out as a promising alternative to conventional lens-based systems.<n>Traditional lensless techniques often require explicit calibrations and extensive pre-processing.<n>We propose LensNet, an end-to-end deep learning framework that integrates spatial-domain and frequency-domain representations.
  arXiv  Detail & Related papers  (2025-05-03T09:11:52Z)
• Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors [52.195637608631955]
  Non-line-of-sight (NLOS) imaging has attracted increasing attention due to its potential applications. Existing NLOS reconstruction approaches are constrained by the reliance on empirical physical priors. We introduce a novel learning-based solution, comprising two key designs: Learnable Path Compensation (LPC) and Adaptive Phasor Field (APF)
  arXiv  Detail & Related papers  (2024-09-21T04:39:45Z)
• OmniLens: Towards Universal Lens Aberration Correction via LensLib-to-Specific Domain Adaptation [28.785118157930167]
  We introduce OmniLens, a flexible solution to universal CAC via (i) establishing a convincing LensLib with comprehensive coverage for pre-training a robust base model, and (ii) adapting the model to any specific lens designs with unknown lens descriptions via fast LensLib-to-specific domain adaptation.<n>Experiments demonstrate that the LensLib generated by EAOD effectively develops a universal CAC model with strong generalization capabilities, which can also improve the non-blind lens-specific methods by 0.35-1.81dB in PSNR.
  arXiv  Detail & Related papers  (2024-09-09T17:12:42Z)
• 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)
• Enhancing Low-light Light Field Images with A Deep Compensation Unfolding Network [52.77569396659629]
  This paper presents the deep compensation network unfolding (DCUNet) for restoring light field (LF) images captured under low-light conditions. The framework uses the intermediate enhanced result to estimate the illumination map, which is then employed in the unfolding process to produce a new enhanced result. To properly leverage the unique characteristics of LF images, this paper proposes a pseudo-explicit feature interaction module.
  arXiv  Detail & Related papers  (2023-08-10T07:53:06Z)
• Mobile Image Restoration via Prior Quantization [15.577548135102404]
  We propose a prior quantization model to correct the optical aberrations in image processing systems. Our model promises to analyze the correlation between the various priors and the optical aberration of devices.
  arXiv  Detail & Related papers  (2023-05-10T05:05:58Z)
• 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)

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.