Thank you for reaching out! Sure, image quality metrics sound very useful. In the same spirit, we have a no-reference perceptual blur metric coming up: https://github.com/scikit-image/scikit-image/pull/5420
Can you please clarify the use cases you have in mind? Are you familiar with applications using directly these EME, EMEE, AME metrics to measure image quality? And/or are you suggesting using these metrics to benchmark existing/upcoming contrast adjustment algorithms?
If you are ready to contribute code, please head over to https://scikit-image.org/docs/stable/contribute.html and let me know if you run into technical issues. Feel free to ask for help in the chat as well: https://skimage.zulipchat.com
On Sun, Jun 20, 2021 at 3:56 AM Lafith Mattara email@example.com wrote:
Hi, I am new to open source and would like to contribute to scikit image. I couldn't find scikit methods to evaluate contrast measures. Image quality metrics like EME, EMEE, AME etc are helpful in writing contrast enhancement algorithms. I would like to know if it is something scikit-image interested in and if so how should I proceed?
Specifically I am talking about Table 1 of https://doi.org/10.1109/TCE.2013.6626251. It summarises commonly used gray scale measures.
Regards, Lafith Mattara _______________________________________________ scikit-image mailing list -- firstname.lastname@example.org To unsubscribe send an email to email@example.com https://mail.python.org/mailman3/lists/scikit-image.python.org/ Member address: firstname.lastname@example.org