Saturation-Aware Snapshot Compressive Imaging: Theory and Algorithm

TL;DR

This paper develops a theoretical framework for understanding and improving snapshot compressive imaging under saturation conditions, proposing optimized mask designs and a novel reconstruction algorithm that outperform existing methods.

Contribution

It provides the first theoretical analysis of SCI recovery under saturation, introduces a saturation-aware reconstruction method, and offers practical mask design guidelines.

Findings

Optimal Bernoulli mask density is below 0.5 under saturation.

SAPnet outperforms existing PnP-based reconstruction methods.

Theoretical bounds link reconstruction error to mask density and saturation level.

Abstract

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.