Graph and Rank Regularized Matrix Recovery for Snapshot Spectral Image Demosaicing

Snapshot spectral imaging (SSI) is a cutting-edge technology for enabling the efficient acquisition of the spatio-spectral content of dynamic scenes using miniaturized platforms. To achieve this goal, SSI architectures associate each spatial pixel with a specific spectral band, thus introducing a critical trade-off between spatial and spectral resolutions. In this paper, we propose a computational approach for the recovery of high spatial and spectral resolution content from a single exposure or a small number of exposures. We formulate the problem in a novel framework of spectral measurement matrix completion and we develop an efficient low-rank and graph regularized method for SSI demosaicing. Furthermore, we extend state-of-the-art approaches by considering more realistic sampling paradigms that incorporate information related to the spectral profile associated with each pixel. In addition to reconstruction quality, we also investigate the impact of recovery on subsequent analysis tasks, such as classification using state-of-the-art convolutional neural networks. We experimentally validate the merits of the proposed recovery scheme using synthetically generated data from indoor and satellite observations and real data obtained with an Interuniversity MicroElectronics Center (IMEC) visible range SSI camera.