MCALab: Reproducible Research in Signal and Image Decomposition and Inpainting, Computing in Science and Engineering. Morphological component analysis of signals and images has far-reaching applications in science and technology, but some consider it problematic and even intractable. Reproducible research is essential to give MCA a firm scientific foundation. Researchers developed MCALab to demonstrate key MCA concepts and make them available to interested researchers.
Keywords for this software
References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Buades, A.; Lisani, J. L.: Directional filters for color cartoon+texture image and video decomposition (2016) ioport
- Kutyniok, Gitta; Lim, Wang-Q; Reisenhofer, Rafael: ShearLab 3D: faithful digital shearlet transforms based on compactly supported shearlets (2016)
- Lobos, Rodrigo; Silva, Jorge F.; Ortiz, Julián M.; Díaz, Gonzalo; Egaña, Alvaro: Analysis and classification of natural rock textures based on new transform-based features (2016)
- Chabiron, Olivier; Malgouyres, François; Tourneret, Jean-Yves; Dobigeon, Nicolas: Toward fast transform learning (2015)
- Hao, Yan; Xu, Jianlou; Bai, Jian; Han, Yu: Image decomposition combining a total variational filter and a Tikhonov quadratic filter (2015)
- He, Bingsheng; Yuan, Xiaoming; Zhang, Wenxing: A customized proximal point algorithm for convex minimization with linear constraints (2013)
- Chambolle, Antonin; Pock, Thomas: A first-order primal-dual algorithm for convex problems with applications to imaging (2011)