CONVOLT: CONVolutional Operator Learning Toolbox

The CONVolutional Operator Learning Toolbox (CONVOLT) – for Matlab – contains various fast and convergent algorithms that can train sparsifying filters associated with convolutional regularizers from large datasets.

Subdirectories (in alphabetical order):

• CAOL algorithms for convolutional analysis operator learning (CAOL)
• CDL algorithms for convolutional dictionary learning (CDL)
• dataset datasets: Fruit, City
• image_helpers preprocessing toolbox

Relevant papers

CAOL

• Il Yong Chun and Jeffrey A. Fessler, "Convolutional analysis operator learning: Acceleration and convergence," IEEE Trans. Image Process., 29:2108–2122, 2020. arXiv, doi
• Il Yong Chun^*, David Hong^*, Ben Adcock, and Jeffrey A. Fessler, "Convolutional analysis operator learning: Dependence on training data," IEEE Signal Process. Lett., 26(8):1137–1141, Aug. 2019. arxiv, doi
• Il Yong Chun and Jeffrey A. Fessler, "Convolutional analysis operator learning: Application to sparse-view CT," in Proc. Asilomar Conf. on Signals, Syst., and Comput. (invited paper), pp. 1631–1635, Pacific Grove, CA, Oct. 2018. doi

CDL

• Il Yong Chun and Jeffrey A. Fessler, "Convolutional dictionary learning: Acceleration and convergence," IEEE Trans. Image Process., 27(4):1697–1712, Apr. 2018. arXiv, doi
• Il Yong Chun and Jeffrey A. Fessler, "Convergent Convolutional Dictionary Learning using Adaptive Contrast Enhancement (CDL-ACE): Application of CDL to image denoising," in Proc. Sampling Theory and Appl. (SampTA), pp. 460–464, Tallinn, Estonia, Jul. 2017. doi

(The asterisks (*) indicate equal contributions.)

These works were supported in part by NIH grants U01 EB018753 and R01 EB022075, the Keck Foundation, NSF grant IIS 1838179, UM-SJTU Collaborative Research Program, and NSERC grant 611675.