Summary of Learning Enriched Features Via Selective State Spaces Model For Efficient Image Deblurring, by Hu Gao et al.
Learning Enriched Features via Selective State Spaces Model for Efficient Image Deblurring
by Hu Gao, Depeng Dang
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes an efficient image deblurring network that leverages the selective state space model to aggregate enriched and accurate features. The network, called ALGBlock, consists of two primary modules: CLGF (capturing local and global features) and FA (feature aggregation). The CLGF module employs a selective state spaces model for long-range dependency feature capture, while the local branch uses simplified channel attention to reduce local pixel forgetting and channel redundancy. The FA module recalibrates weights during aggregation to enhance local parts. Experimental results show that this method outperforms state-of-the-art approaches on widely used benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to fix blurry images by using a special kind of computer program called an image deblurring network. The new network is designed to work well and be efficient, which means it can process lots of data quickly without getting stuck or slowing down. It uses a combination of different techniques to capture both small and big details in the image, making sure that important information isn’t lost. The results show that this method does better than other methods on common tests. |
Keywords
* Artificial intelligence * Attention
