DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal images
This addresses the challenge of enhancing image resolution in remote sensing for applications like environmental monitoring, though it is incremental as it builds on existing CNN approaches.
The authors tackled the problem of super-resolution from multiple unregistered multitemporal images by proposing a novel CNN-based technique that integrates spatial registration within the network, and their method won the European Space Agency's PROBA-V super-resolution challenge.
Recently, convolutional neural networks (CNN) have been successfully applied to many remote sensing problems. However, deep learning techniques for multi-image super-resolution from multitemporal unregistered imagery have received little attention so far. This work proposes a novel CNN-based technique that exploits both spatial and temporal correlations to combine multiple images. This novel framework integrates the spatial registration task directly inside the CNN, and allows to exploit the representation learning capabilities of the network to enhance registration accuracy. The entire super-resolution process relies on a single CNN with three main stages: shared 2D convolutions to extract high-dimensional features from the input images; a subnetwork proposing registration filters derived from the high-dimensional feature representations; 3D convolutions for slow fusion of the features from multiple images. The whole network can be trained end-to-end to recover a single high resolution image from multiple unregistered low resolution images. The method presented in this paper is the winner of the PROBA-V super-resolution challenge issued by the European Space Agency.