  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.
