U-shaped Vision Mamba for Single Image Dehazing
<|reference_start|>U-shaped Vision Mamba for Single Image Dehazing: 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 $325 \times 325$ resolution image (100FPS) without I/O handling time.<|reference_end|>
@article{zheng2024u-shaped,
    title={U-shaped Vision Mamba for Single Image Dehazing},
    author={Zhuoran Zheng and Chen Wu},
    journal={arXiv preprint arXiv:2402.04139},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.04139},
    primaryClass={cs.CV}
}