Summary of Hierarchical Neural Operator Transformer with Learnable Frequency-aware Loss Prior For Arbitrary-scale Super-resolution, by Xihaier Luo and Xiaoning Qian and Byung-jun Yoon
Hierarchical Neural Operator Transformer with Learnable Frequency-aware Loss Prior for Arbitrary-scale Super-resolution
by Xihaier Luo, Xiaoning Qian, Byung-Jun Yoon
First submitted to arxiv on: 20 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed arbitrary-scale super-resolution method leverages operator learning to enhance the resolution of scientific data, addressing challenges like continuity, multi-scale physics, and high-frequency signals. The hierarchical neural operator employs a Galerkin-type self-attention mechanism for efficient function space mapping. Sinc filters facilitate information transfer across levels, ensuring representation equivalence. A learnable prior structure is introduced, derived from spectral resizing of the input data, to balance gradients effectively across the model. Extensive experiments on diverse datasets demonstrate consistent improvements compared to strong baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to make scientific data clearer and more detailed. It uses a special type of artificial intelligence called operator learning to help with this task. The method is good at dealing with complex problems like moving from low-resolution to high-resolution data, and it works well on different types of datasets. |
Keywords
» Artificial intelligence » Self attention » Super resolution
