FD-Vision Mamba for Endoscopic Exposure Correction

TL;DR

FD-Vision Mamba (FDVM-Net) is a novel frequency-domain neural network designed to correct exposure abnormalities in endoscopic images, leveraging a two-path architecture and state space sequence models for superior speed and accuracy.

Contribution

Introduces FDVM-Net, a frequency-domain network with C-SSM blocks for endoscopic exposure correction, capable of handling arbitrary resolutions with improved performance.

Findings

Achieves state-of-the-art speed and accuracy.

Effectively enhances images of arbitrary resolution.

Demonstrates superior performance over existing methods.

Abstract

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}.