# Universal and Flexible Optical Aberration Correction Using Deep-Prior Based Deconvolution

arXiv: https://arxiv.org/abs/2104.03078 · HTML: https://mlsanity.com/paper/2104.03078/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 7 Apr 2021.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better](https://mlsanity.com/paper/1908.03826/) (2019)
- [The Little Engine that Could: Regularization by Denoising (RED)](https://mlsanity.com/paper/1611.02862/) (2016)
- [Deep Unfolding Network for Image Super-Resolution](https://mlsanity.com/paper/2003.10428/) (2020)
- [Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation](https://mlsanity.com/paper/2003.13659/) (2020)
- [Deep Stacked Hierarchical Multi-patch Network for Image Deblurring](https://mlsanity.com/paper/1904.03468/) (2019)
- [Plug-and-Play Methods Provably Converge with Properly Trained Denoisers](https://mlsanity.com/paper/1905.05406/) (2019)
- [Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal](https://mlsanity.com/paper/1503.00593/) (2015)
- [Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging](https://mlsanity.com/paper/2003.13654/) (2020)
- [One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models](https://mlsanity.com/paper/1703.09912/) (2017)
- [DWDN: Deep Wiener Deconvolution Network for Non-Blind Image Deblurring](https://mlsanity.com/paper/2103.09962/) (2021)
- [Learning Fully Convolutional Networks for Iterative Non-blind Deconvolution](https://mlsanity.com/paper/1611.06495/) (2016)
- [End-to-end Interpretable Learning of Non-blind Image Deblurring](https://mlsanity.com/paper/2007.01769/) (2020)

## Built on by (most cited first, 19 of 19)

- [Fast Two-step Blind Optical Aberration Correction](https://mlsanity.com/paper/2208.00950/) (2022)
- [Minimalist and High-Quality Panoramic Imaging with PSF-aware Transformers](https://mlsanity.com/paper/2306.12992/) (2023)
- [Thin On-Sensor Nanophotonic Array Cameras](https://mlsanity.com/paper/2308.02797/) (2023)
- [Annular Computational Imaging: Capture Clear Panoramic Images through Simple Lens](https://mlsanity.com/paper/2206.06070/) (2022)
- [Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision](https://mlsanity.com/paper/2109.08880/) (2021)
- [Computational Imaging for Machine Perception: Transferring Semantic Segmentation beyond Aberrations](https://mlsanity.com/paper/2211.11257/) (2022)
- [Revealing the preference for correcting separated aberrations in joint optic-image design](https://mlsanity.com/paper/2309.04342/) (2023)
- [Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching](https://mlsanity.com/paper/2506.14605/) (2025)
- [Degradation-Modeled Multipath Diffusion for Tunable Metalens Photography](https://mlsanity.com/paper/2506.22753/) (2025)
- [OmniLens: Towards Universal Lens Aberration Correction via LensLib-to-Specific Domain Adaptation](https://mlsanity.com/paper/2409.05809/) (2024)
- [Successive optimization of optics and post-processing with differentiable coherent PSF operator and field information](https://mlsanity.com/paper/2412.14603/) (2024)
- [Learning Latent Transmission and Glare Maps for Lens Veiling Glare Removal](https://mlsanity.com/paper/2511.17353/) (2025)
- [ChromaCorrect: Prescription Correction in Virtual Reality Headsets through Perceptual Guidance](https://mlsanity.com/paper/2212.04264/) (2022)
- [Aberration Correcting Vision Transformers for High-Fidelity Metalens Imaging](https://mlsanity.com/paper/2412.04591/) (2024)
- [Towards Blind Lens Aberration Correction via Large LensLib Pre-training and Discrete Degradation Priors](https://mlsanity.com/paper/2511.17126/) (2025)
- [Neural Invertible Variable-degree Optical Aberrations Correction](https://mlsanity.com/paper/2304.05564/) (2023)
- [DCA-LUT: Deep Chromatic Alignment with 5D LUT for Purple Fringing Removal](https://mlsanity.com/paper/2511.12066/) (2025)
- [Towards Universal Computational Aberration Correction in Photographic Cameras: A Comprehensive Benchmark Analysis](https://mlsanity.com/paper/2603.12083/) (2026)
- [LensBridge: Frequency-Guided Compound Degradation Adaptation for Lens Aberration Correction and Veiling Glare Removal](https://mlsanity.com/paper/2610.00318/) (2026)

## Abstract (arXiv)

High quality imaging usually requires bulky and expensive lenses to compensate geometric and chromatic aberrations. This poses high constraints on the optical hash or low cost applications. Although one can utilize algorithmic reconstruction to remove the artifacts of low-end lenses, the degeneration from optical aberrations is spatially varying and the computation has to trade off efficiency for performance. For example, we need to conduct patch-wise optimization or train a large set of local deep neural networks to achieve high reconstruction performance across the whole image. In this paper, 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, thus leading to a universal and flexible optical aberration correction method. 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, which largely alleviates the time and memory consumption of model learning. The approach is of high efficiency in both training and testing stages. Extensive results verify the promising applications of our proposed approach for compact low-end cameras.