# Optical Aberration Correction in Postprocessing using Imaging Simulation

arXiv: https://arxiv.org/abs/2305.05867 · HTML: https://mlsanity.com/paper/2305.05867/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 77 references that are in the map.
- First posted on arXiv 10 May 2023.

## Builds on (most cited first, 29 of 29)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [EDVR: Video Restoration with Enhanced Deformable Convolutional Networks](https://mlsanity.com/paper/1905.02716/) (2019)
- [Learning to See in the Dark](https://mlsanity.com/paper/1805.01934/) (2018)
- [Learning Deep CNN Denoiser Prior for Image Restoration](https://mlsanity.com/paper/1704.03264/) (2017)
- [Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model](https://mlsanity.com/paper/1904.00523/) (2019)
- [Scale-recurrent Network for Deep Image Deblurring](https://mlsanity.com/paper/1802.01770/) (2018)
- [Unprocessing Images for Learned Raw Denoising](https://mlsanity.com/paper/1811.11127/) (2018)
- [EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis](https://mlsanity.com/paper/1612.07919/) (2016)
- [DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks](https://mlsanity.com/paper/1704.02470/) (2017)
- [Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal](https://mlsanity.com/paper/1503.00593/) (2015)
- [Attention-guided Network for Ghost-free High Dynamic Range Imaging](https://mlsanity.com/paper/1904.10293/) (2019)
- [Zoom To Learn, Learn To Zoom](https://mlsanity.com/paper/1905.05169/) (2019)
- [Neural Blind Deconvolution Using Deep Priors](https://mlsanity.com/paper/1908.02197/) (2019)
- [Replacing Mobile Camera ISP with a Single Deep Learning Model](https://mlsanity.com/paper/2002.05509/) (2020)
- [Learning to Deblur](https://mlsanity.com/paper/1406.7444/) (2014)
- [Spatial-Adaptive Network for Single Image Denoising](https://mlsanity.com/paper/2001.10291/) (2020)
- [WESPE: Weakly Supervised Photo Enhancer for Digital Cameras](https://mlsanity.com/paper/1709.01118/) (2017)
- [HighEr-Resolution Network for Image Demosaicing and Enhancing](https://mlsanity.com/paper/1911.08098/) (2019)
- [Deep Camera: A Fully Convolutional Neural Network for Image Signal Processing](https://mlsanity.com/paper/1908.09191/) (2019)
- [Fast Point Spread Function Modeling with Deep Learning](https://mlsanity.com/paper/1801.07615/) (2018)

## Built on by (most cited first, 18 of 18)

- [End-to-End Hybrid Refractive-Diffractive Lens Design with Differentiable Ray-Wave Model](https://mlsanity.com/paper/2406.00834/) (2024)
- [Optical Generative Models](https://mlsanity.com/paper/2410.17970/) (2024)
- [Exploring Quasi-Global Solutions to Compound Lens Based Computational Imaging Systems](https://mlsanity.com/paper/2404.19201/) (2024)
- [Revealing the preference for correcting separated aberrations in joint optic-image design](https://mlsanity.com/paper/2309.04342/) (2023)
- [A Differentiable Wave Optics Model for End-to-End Computational Imaging System Optimization](https://mlsanity.com/paper/2412.09774/) (2024)
- [A Physics-Informed Blur Learning Framework for Imaging Systems](https://mlsanity.com/paper/2502.11382/) (2025)
- [OmniLens: Towards Universal Lens Aberration Correction via LensLib-to-Specific Domain Adaptation](https://mlsanity.com/paper/2409.05809/) (2024)
- [Efficient Depth- and Spatially-Varying Image Simulation for Defocus Deblur](https://mlsanity.com/paper/2507.00372/) (2025)
- [Successive optimization of optics and post-processing with differentiable coherent PSF operator and field information](https://mlsanity.com/paper/2412.14603/) (2024)
- [Neuro-inspired automated lens design](https://mlsanity.com/paper/2510.09979/) (2025)
- [Learning Latent Transmission and Glare Maps for Lens Veiling Glare Removal](https://mlsanity.com/paper/2511.17353/) (2025)
- [Guidestar-Free Adaptive Optics with Asymmetric Apertures](https://mlsanity.com/paper/2602.07029/) (2026)
- [Towards Blind Lens Aberration Correction via Large LensLib Pre-training and Discrete Degradation Priors](https://mlsanity.com/paper/2511.17126/) (2025)
- [Collaborative Blind Image Deblurring](https://mlsanity.com/paper/2305.16034/) (2023)
- [CircleFlow: Flow-Guided Camera Blur Estimation using a Circle Grid Target](https://mlsanity.com/paper/2512.00796/) (2025)
- [Towards Universal Computational Aberration Correction in Photographic Cameras: A Comprehensive Benchmark Analysis](https://mlsanity.com/paper/2603.12083/) (2026)
- [Realistic Compound-Lens Defocus Blur Synthesis](https://mlsanity.com/paper/2607.05837/) (2026)
- [LensBridge: Frequency-Guided Compound Degradation Adaptation for Lens Aberration Correction and Veiling Glare Removal](https://mlsanity.com/paper/2610.00318/) (2026)

## Abstract (arXiv)

As the popularity of mobile photography continues to grow, considerable effort is being invested in the reconstruction of degraded images. Due to the spatial variation in optical aberrations, which cannot be avoided during the lens design process, recent commercial cameras have shifted some of these correction tasks from optical design to postprocessing systems. However, without engaging with the optical parameters, these systems only achieve limited correction for aberrations.In this work, we propose a practical method for recovering the degradation caused by optical aberrations. Specifically, we establish an imaging simulation system based on our proposed optical point spread function model. Given the optical parameters of the camera, it generates the imaging results of these specific devices. To perform the restoration, we design a spatial-adaptive network model on synthetic data pairs generated by the imaging simulation system, eliminating the overhead of capturing training data by a large amount of shooting and registration. Moreover, we comprehensively evaluate the proposed method in simulations and experimentally with a customized digital-single-lens-reflex (DSLR) camera lens and HUAWEI HONOR 20, respectively. The experiments demonstrate that our solution successfully removes spatially variant blur and color dispersion. When compared with the state-of-the-art deblur methods, the proposed approach achieves better results with a lower computational overhead. Moreover, the reconstruction technique does not introduce artificial texture and is convenient to transfer to current commercial cameras. Project Page: urlhttps://github.com/TanGeeGo/ImagingSimulation.