Summary of Mamba-uie: Enhancing Underwater Images with Physical Model Constraint, by Song Zhang et al.
Mamba-UIE: Enhancing Underwater Images with Physical Model Constraint
by Song Zhang, Yuqing Duan, Daoliang Li, Ran Zhao
First submitted to arxiv on: 27 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 a physical model constraint-based underwater image enhancement framework, Mamba-UIE. The authors address limitations in convolutional neural networks (CNNs) and Transformers for modeling long-range dependencies and recovering global features in underwater images. They decompose input images into four components, reassemble them according to an revised underwater image formation model, and apply a reconstruction consistency constraint to achieve physical constraint on the enhancement process. To tackle quadratic computational complexity of Transformers, they introduce Mamba-UIE networks based on linear complexity state space models, incorporating them with CNN backbones to recover local features and details. The authors demonstrate the effectiveness of their method on three public datasets, achieving a PSNR of 27.13 and an SSIM of 0.93 on the UIEB dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making underwater images look better. Right now, computers struggle to fix blurry or distorted pictures taken underwater because they can’t understand how light behaves in water. The authors created a new way to enhance these images by breaking them down into smaller parts and reassembling them using a special model that mimics how light works underwater. They also developed a way to make the process more efficient, so it doesn’t take too long to improve the images. The results show that their method is much better than others at making underwater images look sharp and clear. |
Keywords
» Artificial intelligence » Cnn
