Combining Low-Rank and Deep Plug-and-Play Priors for Snapshot Compressive Imaging.

Abstract

Snapshot compressive imaging (SCI) is a promising technique that captures a 3-D hyperspectral image (HSI) by a 2-D detector in a compressed manner. The ill-posed inverse process of reconstructing the HSI from their corresponding 2-D measurements is challenging. However, current approaches either neglect the underlying characteristics, such as high spectral correlation, or demand abundant training datasets, resulting in an inadequate balance among performance, generalizability, and interpretability. To address these challenges, in this article, we propose a novel approach called LR2DP that integrates the model-driven low-rank prior and data-driven deep priors for SCI reconstruction. This approach not only captures the spectral correlation and deep spatial features of HSI but also takes advantage of both model-based and learning-based methods without requiring any extra training datasets. Specifically, to preserve the strong spectral correlation of the HSI effectively, we propose that the HSI lies in a low-rank subspace, thereby transforming the problem of reconstructing the HSI into estimating the spectral basis and spatial representation coefficient. Inspired by the mutual promotion of unsupervised deep image prior (DIP) and trained deep denoising prior (DDP), we integrate the unsupervised network and pre-trained deep denoiser into the plug-and-play (PnP) regime to estimate the representation coefficient together, aiming to explore the internal target image prior (learned by DIP) and the external training image prior (depicted by pre-trained DDP) of the HSI. An effective half-quadratic splitting (HQS) technique is employed to optimize the proposed HSI reconstruction model. Extensive experiments on both simulated and real datasets demonstrate the superiority of the proposed method over the state-of-the-art approaches.