Online Convolutional Dictionary Learning for Multimodal Imaging
This work addresses computational imaging for multimodal systems, such as joint intensity-depth imaging, but appears incremental as it builds on existing sparse representation and regularization techniques.
The paper tackles the problem of reconstructing multimodal images from linear measurements by exploiting cross-modal redundancies, achieving high-quality imaging through a combination of convolutional group-sparse representation and total variation regularization, with an online algorithm enabling unsupervised learning on large-scale datasets.
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.