Mamba-UNet 系列论文： U-shaped Vision Mamba for Single Image Dehazing Mamba-UNet - UNet-Like Pure Visual Mamba for Medical Image Segmentation U-Mamba - Enhancing Long-range Dependency for Biomedical Image Segmentation
论文信息
Title: U-shaped Vision Mamba for Single Image Dehazing
Authors: ZhuoranZheng, ChenWu
期刊:
类别: preprint
Level:
- Url: Open online
- zotero entry: Full Text PDF
- open pdf: zotero
Abstract:
Currently, Transformer is the most popular architecture for image dehazing, but due to its large computational complexity, its ability to handle long-range dependency is limited on resource-constrained devices. To tackle this challenge, we introduce the U-shaped Vision Mamba (UVM-Net), an efficient single-image dehazing network. Inspired by the State Space Sequence Models (SSMs), a new deep sequence model known for its power to handle long sequences, we design a Bi-SSM block that integrates the local feature extraction ability of the convolutional layer with the ability of the SSM to capture long-range dependencies. Extensive experimental results demonstrate the effectiveness of our method. Our method provides a more highly efficient idea of long-range dependency modeling for image dehazing as well as other image restoration tasks. The URL of the code is \url{https://github.com/zzr-idam/UVM-Net}. Our method takes only \textbf{0.009} seconds to infer a resolution image (100FPS) without I/O handling time.
概要
Code: https://github.com/zzr-idam/UVM-Net
用 UNet形状包裹起来的Mamba 网络，首次提出用于去雾的 vision mamba。
CNN+SSM，局部特征+全局依赖性
方法
