FD-Vision Mamba for Endoscopic Exposure Correction
<|reference_start|>FD-Vision Mamba for Endoscopic Exposure Correction: 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}.<|reference_end|>
@article{zheng2024fd-vision,
    title={FD-Vision Mamba for Endoscopic Exposure Correction},
    author={Zhuoran Zheng and Jun Zhang},
    journal={arXiv preprint arXiv:2402.06378},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06378},
    primaryClass={cs.CV}
}