Summary of Watermamba: Visual State Space Model For Underwater Image Enhancement, by Meisheng Guan et al.
WaterMamba: Visual State Space Model for Underwater Image Enhancement
by Meisheng Guan, Haiyong Xu, Gangyi Jiang, Mei Yu, Yeyao Chen, Ting Luo, Yang Song
First submitted to arxiv on: 14 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: None
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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 a novel underwater image enhancement (UIE) method called WaterMamba, which leverages a state space model with linear computational complexity to improve image quality in challenging underwater environments. Building upon convolutional neural networks (CNNs) and Transformers, WaterMamba addresses limitations of existing UIE methods by introducing spatial-channel omnidirectional selective scan (SCOSS) blocks and a multi-scale feedforward network (MSFFN). These components model pixel and channel information flow, allowing for synchronized operations within the SCOSS modules. The authors demonstrate WaterMamba’s effectiveness on various datasets, outperforming state-of-the-art methods while reducing computational resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary WaterMamba is a new way to make underwater images look better. When light travels through water, it gets distorted and lost, making pictures blurry or hard to see. Researchers have been trying to fix this problem using special computer models called convolutional neural networks (CNNs) and Transformers. However, these methods have limitations, like needing lots of computing power or not being very good at handling long distances in the image. To solve this, scientists created a new model called WaterMamba that uses a different approach called state space modeling. This method is faster and better at fixing underwater images than previous methods. |
Keywords
» Artificial intelligence » Feedforward network
