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

Zhangzhao Cha, Dongmei Xu, Yi Tang () and Zuo Jiang ()
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Zhangzhao Cha: School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650500, China
Dongmei Xu: School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650500, China
Yi Tang: School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650500, China
Zuo Jiang: School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650500, China

Mathematics, 2023, vol. 11, issue 7, 1-14

Abstract: 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.

Keywords: deep learning; super-resolution; meta-learning; zero-shot (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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