Snapshot Compressive Imaging (SCI) uses coded masks to compress a 3D data cube into a single 2D snapshot. In practice, multiplexing can push intensities beyond the sensor's dynamic range, producing saturation that violates the linear SCI model and degrades reconstruction. This paper provides the first theoretical characterization of SCI recovery under saturation. We model clipping as an element-wise nonlinearity and derive a finite-sample recovery bound for compression-based SCI that links reconstruction error to mask density and the extent of saturation. The analysis yields a clear design rule: optimal Bernoulli masks use densities below one-half, decreasing further as saturation strengthens. Guided by this principle, we optimize mask patterns and introduce a novel reconstruction framework, Saturation-Aware PnP Net (SAPnet), which explicitly enforces consistency with saturated measurements. Experiments on standard video-SCI benchmarks confirm our theory and demonstrate that SAPnet significantly outperforms existing PnP-based methods.

翻译：快照压缩成像（SCI）利用编码掩码将三维数据立方体压缩为单张二维快照。在实际应用中，多路复用可能导致强度值超出传感器的动态范围，从而产生饱和现象，这不仅违背了线性SCI模型，还会导致重建质量下降。本文首次从理论层面刻画了饱和条件下的SCI恢复问题。我们将裁剪过程建模为逐元素非线性变换，并为基于压缩的SCI推导了有限样本恢复界，该界限将重建误差与掩码密度及饱和程度联系起来。分析得出一条清晰的设计准则：最优伯努利掩码的密度应低于二分之一，且随着饱和程度的增强而进一步降低。基于该原则，我们优化了掩码模式，并提出了一种新颖的重建框架——饱和感知即插即用网络（SAPnet），该框架显式地强制重建结果与饱和测量值保持一致。在标准视频SCI基准测试上的实验验证了我们的理论，并表明SAPnet显著优于现有的基于即插即用的方法。