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Deep Learning for Multiple-Image Super-Resolution
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DOI：10.1109/LGRS.2019.2940483.png)
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Abstract
En 中文
Super-resolution (SR) reconstruction is a process aimed at enhancing the spatial resolution of images, either from a single observation, based on the learned relation between low and high resolution, or from multiple images presenting the same scene. SR is particularly important, if it is not feasible to acquire images at the desired resolution, while there are single or many observations available at lower resolution-this is inherent to a variety of remote sensing scenarios. Recently, we have witnessed substantial improvement in single-image SR attributed to the use of deep neural networks for learning the relation between low and high resolution. Importantly, deep learning has not been widely exploited for multiple-image super-resolution, which benefits from information fusion and in general allows for achieving higher reconstruction accuracy. In this letter, we introduce a new approach to combine the advantages of multiple-image fusion with learning the low-to-high resolution mapping using deep networks. The results of our extensive experiments indicate that the proposed framework outperforms the state-of-the-art SR methods.
Keywords:
Image reconstruction
Spatial resolution
Satellites
Deep learning
Imaging
Gallium nitride
Convolutional neural networks (CNNs)
deep learning
image processing
super resolution (SR)
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