Low-Light Endoscopic Image Enhancement for Healthcare Electronics Using Efficient Multiscale Selective Fusion

As essential healthcare electronics, endoscopy systems suffer from non-uniform illumination due to factors such as their built-in lighting configuration, potentially obscuring critical tissue landmarks and subtle pathologies, thereby reducing diagnostic confidence, especially in deep cavities. To address these issues, endoscopic image enhancement methods are required to preserve structural details and improve tissue contrast while maintaining low computational cost. Convolution-based methods enhance global brightness at the cost of attenuating high-frequency textures due to spectral smoothing effects. To overcome this limitation, we propose a multiscale channel selective fusion network to enhance endoscopic images by discriminatively fusing synergistic features via gated multiscale attention, simultaneously optimizing local discriminability and global consistency. Specifically, a selective fusion module is designed to effectively extract and fuse channel information symmetrically at each scale, enhancing brightness and preserving fine textures. Experiments on the Endo4IE and LEIED endoscopic datasets demonstrate that the proposed method outperforms existing nine state-of-the-art techniques under low-light conditions. It offers an effective method for low-light endoscopic image enhancement in healthcare electronics.