  In endoscopic imaging, the recorded images are prone to exposure
abnormalities, so maintaining high-quality images is important to assist
healthcare professionals in performing decision-making. To overcome this issue,
We design a frequency-domain based network, called FD-Vision Mamba (FDVM-Net),
which achieves high-quality image exposure correction by reconstructing the
frequency domain of endoscopic images. Specifically, inspired by the State
Space Sequence Models (SSMs), we develop a C-SSM block that integrates the
local feature extraction ability of the convolutional layer with the ability of
the SSM to capture long-range dependencies. A two-path network is built using
C-SSM as the basic function cell, and these two paths deal with the phase and
amplitude information of the image, respectively. Finally, a degraded
endoscopic image is reconstructed by FDVM-Net to obtain a high-quality clear
image. Extensive experimental results demonstrate that our method achieves
state-of-the-art results in terms of speed and accuracy, and it is noteworthy
that our method can enhance endoscopic images of arbitrary resolution. The URL
of the code is \url{https://github.com/zzr-idam/FDVM-Net}.
