Rank Minimization for Snapshot Compressive Imaging

TL;DR

This paper introduces a novel rank minimization approach that leverages high-dimensional structures and nonlocal self-similarity to significantly improve the reconstruction quality of snapshot compressive imaging systems.

Contribution

It develops a joint model combining nonlocal self-similarity and rank minimization, along with an efficient alternating minimization algorithm tailored for SCI reconstruction.

Findings

Significant improvement in reconstruction quality over state-of-the-art methods.

Effective handling of computational and memory challenges in SCI.

Validated results on both simulated and real SCI data from multiple cameras.

Abstract

Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple frames are mapped into a single measurement, with video compressive imaging and hyperspectral compressive imaging as two representative applications. Though exciting results of high-speed videos and hyperspectral images have been demonstrated, the poor reconstruction quality precludes SCI from wide applications.This paper aims to boost the reconstruction quality of SCI via exploiting the high-dimensional structure in the desired signal. We build a joint model to integrate the nonlocal self-similarity of video/hyperspectral frames and the rank minimization approach with the SCI sensing process. Following this, an alternating minimization algorithm is developed to solve this non-convex problem. We further investigate the special structure of the sampling process in SCI to tackle the computational workload and memory issues in SCI reconstruction. Both simulation and real data (captured by four different SCI cameras) results demonstrate that our proposed algorithm leads to significant improvements compared with current state-of-the-art algorithms. We hope our results will encourage the researchers and engineers to pursue further in compressive imaging for real applications.