We analyse the convergence of the proximal gradient algorithm for convex composite problems in the presence of gradient and proximal computational inaccuracies.
We propose a method that combines sparse depth (LiDAR) measurements with an intensity image and to produce a dense high-resolution depth image.
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.
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.
We propose and analyze an online algorithm for reconstructing a sequence of signals from a limited number of linear measurements.
Our bounds and geometrical interpretations reveal that if the prior information has good enough quality, L1-L1 minimization improves the performance of CS dramatically.
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