Contributing new algorithms to skimage (PhyCV by UCLA, no patent)

[wesleygwn]

Hey everyone,

Researchers from the Jalali Lab at UCLA are looking to contribute some new computer vision algorithms to scikit-image! We’ve developed these as part of our PhyCV project (GitHub - JalaliLabUCLA/phycv: PhyCV: The First Physics-inspired Computer Vision Library) and we think they could really benefit the community.

Here’s the lowdown:

• VLight: This algorithm offers significantly faster low-light image enhancement with better performance than what’s out there already. Check out our paper for the details: Real-time low-light video enhancement on smartphones | Journal of Real-Time Image Processing

• PhyTex: A robust and accurate approach to edge detection that outperforms traditional techniques. It builds on our Phase-Stretch Transform (PST) from our PhyCV work.

Both VLight and PhyTex are fully open-source and patent-free. We’ve got them implemented in Python, ready to go for scikit-image.

We believe these algorithms would be awesome additions to the library, providing users with cutting-edge tools for image processing and analysis. We’re more than happy to work with the scikit-image team to ensure smooth integration and documentation.

Let us know what you think!

Best,

Wesley Gunawan Jalali Lab @ UCLA

[wesleygwn]

Hi All,

I’ve pushed an implementation of VLight PhyCV to the following branch:

GitHub - ascensionqwer/scikit-image at phycv-vlight

If you see any areas for improvement, please feel free to modify the implementation and communicate your feedback or updates here in this forum. Your input would be greatly appreciated!

I’m also reaching out to the skimage core team members to discuss the possibility of pushing this to the main branch.

Thank you in advance for your time and contributions!

Best regards,
Wesley

[stefanv]

Hi Wesley,

Thanks for your interest in contributing to skimage!

Is the function proposed here different from Gamma correction?

Best regards,
Stéfan