FD-Vision Mamba用于内窥镜曝光校正
FD-Vision Mamba for Endoscopic Exposure Correction
AI总结：
针对内窥镜图像曝光异常问题，提出基于频域重建的FDVM-Net网络，利用C-SSM块结合卷积与SSM处理相位和幅度信息，在速度和精度上达到SOTA，并支持任意分辨率图像增强。
摘要：
在内窥镜成像中，记录的图像容易出现曝光异常，因此保持高质量图像对于辅助医疗专业人员进行决策很重要。为了克服这个问题，我们设计了一个基于频域的网络，称为FD-Vision Mamba (FDVM-Net)，它通过重建内窥镜图像的频域来实现高质量图像曝光校正。具体来说，受State Space Sequence Models (SSMs)的启发，我们开发了一个C-SSM块，它将卷积层的局部特征提取能力与SSM捕获长程依赖的能力相结合。使用C-SSM作为基本功能单元构建了一个双路径网络，这两条路径分别处理图像的相位和幅度信息。最后，FDVM-Net重建退化的内窥镜图像以获得高质量的清晰图像。大量实验结果表明，我们的方法在速度和准确性方面取得了state-of-the-art的结果，值得注意的是，我们的方法可以增强任意分辨率的内窥镜图像。代码的URL是https://github.com/zzr-idam/FDVM-Net。
英文摘要：
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}.
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