Color video snapshot compressive imaging (SCI) employs computational imaging techniques to capture multiple sequential video frames in a single Bayer-patterned measurement. With the increasing popularity of quad-Bayer pattern in mainstream smartphone cameras for capturing high-resolution videos, mobile photography has become more accessible to a wider audience. However, existing color video SCI reconstruction algorithms are designed based on the traditional Bayer pattern. When applied to videos captured by quad-Bayer cameras, these algorithms often result in color distortion and ineffective demosaicing, rendering them impractical for primary equipment. To address this challenge, we propose the MambaSCI method, which leverages the Mamba and UNet architectures for efficient reconstruction of quad-Bayer patterned color video SCI. To the best of our knowledge, our work presents the first algorithm for quad-Bayer patterned SCI reconstruction, and also the initial application of the Mamba model to this task. Specifically, we customize Residual-Mamba-Blocks, which residually connect the Spatial-Temporal Mamba (STMamba), Edge-Detail-Reconstruction (EDR) module, and Channel Attention (CA) module. Respectively, STMamba is used to model long-range spatial-temporal dependencies with linear complexity, EDR is for better edge-detail reconstruction, and CA is used to compensate for the missing channel information interaction in Mamba model. Experiments demonstrate that MambaSCI surpasses state-of-the-art methods with lower computational and memory costs. PyTorch style pseudo-code for the core modules is provided in the supplementary materials.

翻译：彩色视频快照压缩成像（SCI）利用计算成像技术，在单次拜耳模式测量中捕获多帧连续视频。随着四拜耳模式在主流智能手机相机中日益普及以拍摄高分辨率视频，移动摄影已为更广泛的用户群体所使用。然而，现有彩色视频SCI重建算法均基于传统拜耳模式设计。当应用于四拜尔相机拍摄的视频时，这些算法常导致色彩失真与去马赛克失效，使其难以适用于主流设备。为应对这一挑战，我们提出MambaSCI方法，该方法结合Mamba与UNet架构，实现四拜耳模式彩色视频SCI的高效重建。据我们所知，本研究首次提出了针对四拜耳模式SCI重建的算法，亦是Mamba模型在该任务中的首次应用。具体而言，我们定制了残差Mamba模块，该模块通过残差连接整合了时空Mamba模块、边缘细节重建模块与通道注意力模块。其中，时空Mamba模块用于以线性复杂度建模长程时空依赖关系，边缘细节重建模块旨在提升边缘细节重建质量，通道注意力模块则用于补偿Mamba模型中缺失的通道信息交互。实验表明，MambaSCI在降低计算与内存开销的同时，性能优于现有先进方法。核心模块的PyTorch风格伪代码已提供于补充材料中。