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.
@article{arxiv.1706.04256,
title = {Online Convolutional Dictionary Learning for Multimodal Imaging},
author = {Kevin Degraux and Ulugbek S. Kamilov and Petros T. Boufounos and Dehong Liu},
journal= {arXiv preprint arXiv:1706.04256},
year = {2017}
}