Summary of Image Super-resolution with Taylor Expansion Approximation and Large Field Reception, by Jiancong Feng et al.
Image Super-Resolution with Taylor Expansion Approximation and Large Field Reception
by Jiancong Feng, Yuan-Gen Wang, Mingjie Li, Fengchuang Xing
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 In this paper, researchers tackle the problem of computationally expensive self-similarity techniques used in blind super-resolution (SR). The proposed approach, which combines a second-order Taylor expansion approximation (STEA) with multi-scale large field reception (MLFR), aims to reduce computational complexity and improve performance. The STEA method separates the matrix multiplication between Query and Key, reducing the complexity from O(N^2) to O(N). The MLFR design compensates for any performance degradation caused by STEA. The authors demonstrate their approach’s effectiveness on five synthetic datasets and two real-world scenarios, setting a new benchmark in both qualitative and quantitative evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper discusses a method to make blind super-resolution (SR) more efficient. It uses special techniques called self-similarity that help predict how images get distorted. However, these techniques take up too much computer power. The researchers find a way to simplify the calculations, making it faster and better. They test their new approach on some fake data and real-world pictures, showing it works well. |
Keywords
» Artificial intelligence » Super resolution
