  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.
