# Blind Geometric Distortion Correction on Images Through Deep Learning

arXiv: https://arxiv.org/abs/1909.03459 · HTML: https://mlsanity.com/paper/1909.03459/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2019.

## Built on by (most cited first, 28 of 28)

- [Perspective Fields for Single Image Camera Calibration](https://mlsanity.com/paper/2212.03239/) (2022)
- [Progressively Complementary Network for Fisheye Image Rectification Using Appearance Flow](https://mlsanity.com/paper/2103.16026/) (2021)
- [Deep Learning for Camera Calibration and Beyond: A Survey](https://mlsanity.com/paper/2303.10559/) (2023)
- [SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps](https://mlsanity.com/paper/2201.05120/) (2022)
- [Thinking with Camera: A Unified Multimodal Model for Camera-Centric Understanding and Generation](https://mlsanity.com/paper/2510.08673/) (2025)
- [AnyCalib: On-Manifold Learning for Model-Agnostic Single-View Camera Calibration](https://mlsanity.com/paper/2503.12701/) (2025)
- [SimFIR: A Simple Framework for Fisheye Image Rectification with Self-supervised Representation Learning](https://mlsanity.com/paper/2308.09040/) (2023)
- [FishFormer: Annulus Slicing-based Transformer for Fisheye Rectification with Efficacy Domain Exploration](https://mlsanity.com/paper/2207.01925/) (2022)
- [RecRecNet: Rectangling Rectified Wide-Angle Images by Thin-Plate Spline Model and DoF-based Curriculum Learning](https://mlsanity.com/paper/2301.01661/) (2023)
- [Rethinking Generic Camera Models for Deep Single Image Camera Calibration to Recover Rotation and Fisheye Distortion](https://mlsanity.com/paper/2111.12927/) (2021)
- [Water Simulation and Rendering from a Still Photograph](https://mlsanity.com/paper/2210.02553/) (2022)
- [Dual Diffusion Architecture for Fisheye Image Rectification: Synthetic-to-Real Generalization](https://mlsanity.com/paper/2301.11785/) (2023)
- [FisheyeEX: Polar Outpainting for Extending the FoV of Fisheye Lens](https://mlsanity.com/paper/2206.05844/) (2022)
- [Spatiotemporal Deformation Perception for Fisheye Video Rectification](https://mlsanity.com/paper/2302.03934/) (2023)
- [Camera Calibration without Camera Access -- A Robust Validation Technique for Extended PnP Methods](https://mlsanity.com/paper/2302.06949/) (2023)
- [M"obius Transform for Mitigating Perspective Distortions in Representation Learning](https://mlsanity.com/paper/2405.02296/) (2024)
- [AlignDiff: Learning Physically-Grounded Camera Alignment via Diffusion](https://mlsanity.com/paper/2503.21581/) (2025)
- [Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms](https://mlsanity.com/paper/2504.06126/) (2025)
- [QueryCDR: Query-Based Controllable Distortion Rectification Network for Fisheye Images](https://mlsanity.com/paper/2412.13496/) (2024)
- [Robustness of Structured Data Extraction from Perspectively Distorted Documents](https://mlsanity.com/paper/2511.17607/) (2025)
- [Visual Question Answering (VQA) on Images with Superimposed Text](https://mlsanity.com/paper/2307.02489/) (2023)
- [RoFIR: Robust Fisheye Image Rectification Framework Impervious to Optical Center Deviation](https://mlsanity.com/paper/2406.18927/) (2024)
- [LCM: Log Conformal Maps for Robust Representation Learning to Mitigate Perspective Distortion](https://mlsanity.com/paper/2410.03686/) (2024)
- [Predicting the Reliability of an Image Classifier under Image Distortion](https://mlsanity.com/paper/2412.16881/) (2024)
- [A Deep Single Image Rectification Approach for Pan-Tilt-Zoom Cameras](https://mlsanity.com/paper/2504.06965/) (2025)
- [DCA-LUT: Deep Chromatic Alignment with 5D LUT for Purple Fringing Removal](https://mlsanity.com/paper/2511.12066/) (2025)
- [Rectification Reimagined: A Unified Mamba Model for Image Correction and Rectangling with Prompts](https://mlsanity.com/paper/2512.18718/) (2025)
- [Puffin-World: Scaling a Unified Multimodal Model with Native 3D World States](https://mlsanity.com/paper/2609.04196/) (2026)

## Abstract (arXiv)

We propose the first general framework to automatically correct different types of geometric distortion in a single input image. Our proposed method employs convolutional neural networks (CNNs) trained by using a large synthetic distortion dataset to predict the displacement field between distorted images and corrected images. A model fitting method uses the CNN output to estimate the distortion parameters, achieving a more accurate prediction. The final corrected image is generated based on the predicted flow using an efficient, high-quality resampling method. Experimental results demonstrate that our algorithm outperforms traditional correction methods, and allows for interesting applications such as distortion transfer, distortion exaggeration, and co-occurring distortion correction.