Summary of Mpsi: Mamba Enhancement Model For Pixel-wise Sequential Interaction Image Super-resolution, by Yuchun He and Yuhan He
MPSI: Mamba enhancement model for pixel-wise sequential interaction Image Super-Resolution
by Yuchun He, Yuhan He
First submitted to arxiv on: 10 Dec 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 The proposed Mamba pixel-wise sequential interaction network (MPSI) tackles the long-standing challenge of single-image super-resolution by modeling global pixel interactions and capturing comprehensive feature information. The model consists of a Channel-Mamba Block (CMB) that effectively captures long-range connections, as well as the Mamba channel recursion module (MCRM), which retains valuable features from early layers. Experimental results demonstrate that MPSI achieves state-of-the-art performance in image reconstruction, outperforming existing super-resolution methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to improve single-image super-resolution. It uses a special network called MPSI that helps computer vision models understand how pixels are connected across an image. This is important because it can help create more detailed and realistic images from low-quality ones. The authors of the paper also came up with two new blocks, CMB and MCRM, which work together to make the network more effective. By testing their method on different images, they showed that it performs better than other similar methods. |
Keywords
» Artificial intelligence » Super resolution
