TR2017-112

Online Convolutional Dictionary Learning for Multimodal Imaging


Abstract:

Computational imaging methods that can exploit multiple modalities have the potential to enhance the capabilities of traditional sensing systems. In this paper, we propose a new method that reconstructs multimodal images from their linear measurements by exploiting redundancies across different modalities. Our method combines a convolutional group-sparse representation of images with total variation (TV) regularization for high-quality multimodal imaging. We develop an online algorithm that enables the unsupervised learning of convolutional dictionaries on large-scale datasets that are typical in such applications. We illustrate the benefit of our approach in the context of joint intensity-depth imaging.

 

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  •  Degraux, K., Kamilov, U., Boufounos, P.T., Liu, D., "Online Convolutional Dictionary Learning for Multimodal Imaging", arXiv, June 2017.
    BibTeX arXiv
    • @article{Degraux2017jun,
    • author = {Degraux, Kevin and Kamilov, Ulugbek and Boufounos, Petros T. and Liu, Dehong},
    • title = {Online Convolutional Dictionary Learning for Multimodal Imaging},
    • journal = {arXiv},
    • year = 2017,
    • month = jun,
    • url = {https://arxiv.org/abs/1706.04256}
    • }