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}.

翻译：在内窥镜成像中，记录图像易出现曝光异常，因此保持高质量图像对于辅助医疗专业人员做出决策至关重要。为解决该问题，我们设计了一种基于频域的网络，称为FD-Vision Mamba（FDVM-Net），通过重构内窥镜图像的频域实现高质量图像曝光校正。具体而言，受状态空间序列模型（SSMs）启发，我们开发了C-SSM模块，该模块融合了卷积层的局部特征提取能力与SSM捕捉长距离依赖的能力。以C-SSM作为基本功能单元构建双路径网络，这两条路径分别处理图像的相位信息和幅度信息。最终，通过FDVM-Net对退化内窥镜图像进行重构，获得高质量清晰图像。大量实验结果表明，我们的方法在速度和精度上均达到了最先进水平，值得注意的是，该方法可增强任意分辨率的内窥镜图像。代码地址为：\url{https://github.com/zzr-idam/FDVM-Net}。