Saturation in Snapshot Compressive Imaging

Snapshot Compressive Imaging (SCI) maps three-dimensional (3D) data cubes, such as videos or hyperspectral images, into two-dimensional (2D) measurements via optical modulation, enabling efficient data acquisition and reconstruction. Recent advances have shown the potential of mask optimization to enhance SCI performance, but most studies overlook nonlinear distortions caused by saturation in practical systems. Saturation occurs when high-intensity measurements exceed the sensor's dynamic range, leading to information loss that standard reconstruction algorithms cannot fully recover. This paper addresses the challenge of optimizing binary masks in SCI under saturation. We theoretically characterize the performance of compression-based SCI recovery in the presence of saturation and leverage these insights to optimize masks for such conditions. Our analysis reveals trade-offs between mask statistics and reconstruction quality in saturated systems. Experimental results using a Plug-and-Play (PnP) style network validate the theory, demonstrating improved recovery performance and robustness to saturation with our optimized binary masks.