  Convolutional neural networks (CNNs) and Vision Transformers (ViTs) have
achieved excellent performance in image restoration. ViTs typically yield
superior results in image restoration compared to CNNs due to their ability to
capture long-range dependencies and input-dependent characteristics. However,
the computational complexity of Transformer-based models grows quadratically
with the image resolution, limiting their practical appeal in high-resolution
image restoration tasks. In this paper, we propose a simple yet effective
visual state space model (EVSSM) for image deblurring, leveraging the benefits
of state space models (SSMs) to visual data. In contrast to existing methods
that employ several fixed-direction scanning for feature extraction, which
significantly increases the computational cost, we develop an efficient visual
scan block that applies various geometric transformations before each SSM-based
module, capturing useful non-local information and maintaining high efficiency.
Extensive experimental results show that the proposed EVSSM performs favorably
against state-of-the-art image deblurring methods on benchmark datasets and
real-captured images.
