Convolutional group-sparse coding and source localization
Signal Processing
2018-10-22 v1
Abstract
In this paper, we present a new interpretation of non-negatively constrained convolutional coding problems as blind deconvolution problems with spatially variant point spread function. In this light, we propose an optimization framework that generalizes our previous work on non-negative group sparsity for convolutional models. We then link these concepts to source localization problems that arise in scientific imaging and provide a visual example on an image derived from data captured by the Hubble telescope.
Keywords
Cite
@article{arxiv.1810.08432,
title = {Convolutional group-sparse coding and source localization},
author = {Pol del Aguila Pla and Joakim Jaldén},
journal= {arXiv preprint arXiv:1810.08432},
year = {2018}
}
Comments
5 pages, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 15-20 April 2018, Calgary, AB, Canada