Effective Snapshot Compressive-spectral Imaging via Deep Denoising and Total Variation Priors
Haiquan Qiu, Yao Wang, Deyu Meng
Abstract
Snapshot compressive imaging (SCI) is a new type of compressive imaging system that compresses multiple frames of images into a single snapshot measurement, which enjoys low cost, low bandwidth, and high-speed sensing rate. By applying the existing SCI methods to deal with hyperspectral images, however, could not fully exploit the underlying structures, and thereby demonstrate unsatisfactory reconstruction performance. To remedy such issue, this paper aims to propose a new effective method by taking advantage of two intrinsic priors of the hyperspectral images, namely deep image denoising and total variation (TV) priors. Specifically, we propose an optimization objective to utilize these two priors. By solving this optimization objective, our method is equivalent to incorporate a weighted FFDNet and a 2DTV or 3DTV denoiser into the plug-andplay framework. Extensive numerical experiments demonstrate the outperformance of the proposed method over several state-of-the-art alternatives. Additionally, we provide a detailed convergence analysis of the resulting plug-andplay algorithm under relatively weak conditions such as without using diminishing step sizes. The code is available at https://github.com/ucker/SCI-TVFFDNet.