Meta-Learning for Zero-Shot Remote Sensing Image Super-Resolution

Zero-shot super-resolution (ZSSR) has generated a lot of interest due to its flexibility in various applications. However, the computational demands of ZSSR make it ineffective when dealing with large-scale low-resolution image sets. To address this issue, we propose a novel meta-learning model. We treat the set of low-resolution images as a collection of ZSSR tasks and learn meta-knowledge about ZSSR by leveraging these tasks. This approach reduces the computational burden of super-resolution for large-scale low-resolution images. Additionally, through multiple ZSSR task learning, we uncover a general super-resolution model that enhances the generalization capacity of ZSSR. Finally, using the learned meta-knowledge, our model achieves impressive results with just a few gradient updates when given a novel task. We evaluate our method using two remote sensing datasets with varying spatial resolutions. Our experimental results demonstrate that using multiple ZSSR tasks yields better outcomes than a single task, and our method outperforms other state-of-the-art super-resolution methods.

Automated summary

Zero-shot super-resolution (ZSSR) has attracted much attention for its flexibility in various applications, but is ineffective for large-scale low-resolution image sets due to computational demands. To address this, we propose a novel meta-learning model that treats low-resolution images as ZSSR tasks and learns meta-knowledge. This approach reduces the computational burden and enhances the generalization capacity of ZSSR. Experimental results demonstrate its impressive performance and superiority over other state-of-the-art methods.