2 code implementations • 22 Aug 2014 • Joao F. C. Mota, Nikos Deligiannis, Miguel R. D. Rodrigues
Our bounds and geometrical interpretations reveal that if the prior information has good enough quality, L1-L1 minimization improves the performance of CS dramatically.
Information Theory Information Theory
2 code implementations • 11 Mar 2015 • Joao F. C. Mota, Nikos Deligiannis, Aswin C. Sankaranarayanan, Volkan Cevher, Miguel R. D. Rodrigues
We propose and analyze an online algorithm for reconstructing a sequence of signals from a limited number of linear measurements.
no code implementations • 14 Jul 2016 • Nikos Deligiannis, Joao F. C. Mota, Bruno Cornelis, Miguel R. D. Rodrigues, Ingrid Daubechies
Our dictionary learning framework can be tailored both to a single- and a multi-scale framework, with the latter leading to a significant performance improvement.
1 code implementation • 26 Jun 2018 • Pingfan Song, Lior Weizman, Joao F. C. Mota, Yonina C. Eldar, Miguel R. D. Rodrigues
In this paper, we propose a Coupled Dictionary Learning based multi-contrast MRI reconstruction (CDLMRI) approach to leverage an available guidance contrast to restore the target contrast.
no code implementations • 31 Oct 2019 • Alireza Ahrabian, Joao F. C. Mota, Andrew M. Wallace
We propose a method that combines sparse depth (LiDAR) measurements with an intensity image and to produce a dense high-resolution depth image.
no code implementations • 4 Mar 2022 • Anis Hamadouche, Yun Wu, Andrew M. Wallace, Joao F. C. Mota
We analyse the convergence of the proximal gradient algorithm for convex composite problems in the presence of gradient and proximal computational inaccuracies.
no code implementations • 31 Mar 2023 • Angel Victor Juanco Muller, Joao F. C. Mota, Keith A. Goatman, Corne Hoogendoorn
However, we show that by including an orientation score guided convolutional module, based on the anisotropic single sided cake wavelet, we reduce such misclassification and further increase the topology correctness of the results.
no code implementations • 17 Apr 2023 • Angel Victor Juanco Muller, Joao F. C. Mota, Keith A. Goatman, Corne Hoogendoorn
First, the scans with arbitrary FoV are cropped to the head and neck region and a u-shaped convolutional neural network (CNN) is trained to segment the region of interest.