# A Physics-Informed Blur Learning Framework for Imaging Systems

arXiv: https://arxiv.org/abs/2502.11382 · HTML: https://mlsanity.com/paper/2502.11382/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 50 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 19 of 19)

- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [Multi-Stage Progressive Image Restoration](https://mlsanity.com/paper/2102.02808/) (2021)
- [MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2204.08958/) (2022)
- [Conflict-Averse Gradient Descent for Multi-task Learning](https://mlsanity.com/paper/2110.14048/) (2021)
- [Unprocessing Images for Learned Raw Denoising](https://mlsanity.com/paper/1811.11127/) (2018)
- [Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring](https://mlsanity.com/paper/2211.12250/) (2022)
- [Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels](https://mlsanity.com/paper/1903.12529/) (2019)
- [Mobile Computational Photography: A Tour](https://mlsanity.com/paper/2102.09000/) (2021)
- [Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution](https://mlsanity.com/paper/2108.05302/) (2021)
- [Fast Two-step Blind Optical Aberration Correction](https://mlsanity.com/paper/2208.00950/) (2022)
- [Optical Aberration Correction in Postprocessing using Imaging Simulation](https://mlsanity.com/paper/2305.05867/) (2023)
- [Polyblur: Removing mild blur by polynomial reblurring](https://mlsanity.com/paper/2012.09322/) (2020)
- [Computational Optics for Mobile Terminals in Mass Production](https://mlsanity.com/paper/2305.05886/) (2023)
- [Revealing the preference for correcting separated aberrations in joint optic-image design](https://mlsanity.com/paper/2309.04342/) (2023)
- [Learning Lens Blur Fields](https://mlsanity.com/paper/2310.11535/) (2023)
- [Rethinking data-driven point spread function modeling with a differentiable optical model](https://mlsanity.com/paper/2203.04908/) (2022)

## Built on by (most cited first, 5 of 5)

- [Learning Latent Transmission and Glare Maps for Lens Veiling Glare Removal](https://mlsanity.com/paper/2511.17353/) (2025)
- [Towards Blind Lens Aberration Correction via Large LensLib Pre-training and Discrete Degradation Priors](https://mlsanity.com/paper/2511.17126/) (2025)
- [CircleFlow: Flow-Guided Camera Blur Estimation using a Circle Grid Target](https://mlsanity.com/paper/2512.00796/) (2025)
- [RAW-Domain Degradation Models for Realistic Smartphone Super-Resolution](https://mlsanity.com/paper/2603.12493/) (2026)
- [Realistic Compound-Lens Defocus Blur Synthesis](https://mlsanity.com/paper/2607.05837/) (2026)

## Abstract (arXiv)

Accurate blur estimation is essential for high-performance imaging across various applications. Blur is typically represented by the point spread function (PSF). In this paper, we propose a physics-informed PSF learning framework for imaging systems, consisting of a simple calibration followed by a learning process. Our framework could achieve both high accuracy and universal applicability. Inspired by the Seidel PSF model for representing spatially varying PSF, we identify its limitations in optimization and introduce a novel wavefront-based PSF model accompanied by an optimization strategy, both reducing optimization complexity and improving estimation accuracy. Moreover, our wavefront-based PSF model is independent of lens parameters, eliminate the need for prior knowledge of the lens. To validate our approach, we compare it with recent PSF estimation methods (Degradation Transfer and Fast Two-step) through a deblurring task, where all the estimated PSFs are used to train state-of-the-art deblurring algorithms. Our approach demonstrates improvements in image quality in simulation and also showcases noticeable visual quality improvements on real captured images.