A Mamba U-Net Model for Reconstruction of Extremely Dark RGGB Images
Yiyao Huang, Xiaobao Zhu, Fenglian Yuan, Jing Shi, U KinTak, Junshuo Qin, Xiangjie Kong, Yiran Peng · Sensors · 2025
Currently, most images captured by high-pixel devices such as mobile phones, camcorders, and drones are in RGGB format. However, image quality in extremely dark scenes often needs improvement. Traditional methods for processing these dark RGGB images typically rely on end-to-end U-Net networks and their enhancement techniques, which require substantial resources and processing time. To tackle this issue, we first converted RGGB images into RGB three-channel images by subtracting the black level and applying linear interpolation. During the training stage, we leveraged the computational efficiency of the state-space model (SSM) and developed a Mamba U-Net end-to-end model to enhance the restoration of extremely dark RGGB images. We utilized the see-in-the-dark (SID) dataset for training, assessing the effectiveness of our approach. Experimental results indicate that our method significantly reduces resource consumption compared to existing single-step training and prior multi-step training techniques, while achieving improved peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) outcomes.