Affiliation:
1. School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650500, China
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.
Funder
National Natural Science Foundation of China
Special Support Plan for High-Level Talents of Guangdong Province
Project of Guangdong Province Innovative Team
Subject
General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)
Reference48 articles.
1. Super-Resolution Enhanced Medical Image Diagnosis with Sample Affinity Interaction;Chen;IEEE Trans. Med Imaging,2021
2. Remote sensing images super-resolution with deep convolution networks;Ran;Multimed. Tools Appl.,2020
3. Zhang, M., Xin, J., Zhang, J., Tao, D., and Gao, X. (2022). Curvature Consistent Network for Microscope Chip Image Super-Resolution. IEEE Trans. Neural Networks Learn. Syst., 1–14.
4. Adversarial Examples Generation for Deep Product Quantization Networks on Image Retrieval;Chen;IEEE Trans. Pattern Anal. Mach. Intell.,2023
5. A One-Stage Domain Adaptation Network with Image Alignment for Unsupervised Nighttime Semantic Segmentation;Wu;IEEE Trans. Pattern Anal. Mach. Intell.,2023
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