Perdios, DimitrisBesson, Adrien Georges JeanRossinelli, PhilippeThiran, Jean-Philippe2017-02-162017-02-162017-02-16201710.1109/ICIP.2017.8296844https://infoscience.epfl.ch/handle/20.500.14299/134335We propose to map the fast iterative shrinkage-thresholding algorithm to a deep neural network (DNN), with a sparsity prior in a concatenation of wavelet bases, in the context of compressive imaging. We exploit the DNN architecture to learn the optimal weight matrix of the corresponding reweighted l1-minimization problem. We later use the learned weight matrix for the image reconstruction process, which is recast as a simple l1-minimization problem. The approach, denoted as learned extended FISTA, shows promising results in terms of image quality, compared to state-of-the-art algorithms, and significantly reduces the reconstruction time required to solve the reweighted l1-minimization problem.Compressed sensingDeep learningFast iterative shrinkage-thresholding algorithmultrasoundtext::conference output::conference paper not in proceedings