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.
The work of REF instead learns groupsparse representations and uses total variation (TV) regularization for depth SR reconstruction.
Online convolutional dictionary learning for multimodal imaging
2017 IEEE International Conference on Image Processing (ICIP)
2017 IEEE International Conference on Image Processing (ICIP)
Computer Science