Question 1

How does Awesome Image Distortion Correction compare to other computer vision paper lists?

Accepted Answer

It specializes deeply in distortion correction methods, offering focused coverage of rolling shutter, radial, and text distortions, whereas broader resources like paperswithcode.com cover all CV topics with less niche depth. This makes it better for targeted research but less useful for general learning.

Question 2

How to implement rolling shutter correction using code from this list?

Accepted Answer

Identify a relevant paper in the Rolling-shutter-Effects section, check for a linked code repository (e.g., from CVPR 2021's 'SUNet'), and follow the repository's README for setup. You'll likely need to install dependencies, prepare your dataset, and adapt the code to your specific hardware and software environment.

Question 3

What are the best datasets for training fisheye distortion correction models?

Accepted Answer

The repository lists several datasets under the Datasets section, such as the synthetic fisheye dataset from a 2018 arXiv paper. Look for entries with direct links, and note that availability varies—some may require request or have specific usage terms, so check the original sources.

Question 4

Can I use these methods for real-time video correction on mobile devices?

Accepted Answer

Some linked code, like deep learning approaches from recent CVPR papers, may be computationally intensive. You'll need to evaluate each method's performance and optimize for real-time use, as the repository doesn't provide latency or efficiency benchmarks.

Question 5

How to contribute new papers or code to this awesome list?

Accepted Answer

Submit a pull request via GitHub with the item formatted in the existing table style, including year, conference, title, and link. Ensure it fits the categorized distortion types and follows the project's open knowledge sharing principle.

Question 6

Is there a benchmark for comparing different text distortion rectification algorithms?

Accepted Answer

No, the repository doesn't include benchmarks. You should refer to the original papers, such as those from CVPR 2019 on scene text recognition, which may have evaluation metrics, or look for independent community benchmarks elsewhere.

Awesome Image Distortion Correction

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