Self-supervised Neural Networks for Spectral Snapshot Compressive Imaging

TL;DR

This paper introduces a self-supervised neural network approach for spectral snapshot compressive imaging reconstruction, eliminating the need for training data and achieving competitive or state-of-the-art results.

Contribution

The paper develops a novel self-supervised framework integrating deep image priors into SCI reconstruction, bypassing the need for large training datasets.

Findings

Competitive results without training data

State-of-the-art performance with pre-trained denoising prior

Effective on synthetic and real data

Abstract

We consider using {\bf\em untrained neural networks} to solve the reconstruction problem of snapshot compressive imaging (SCI), which uses a two-dimensional (2D) detector to capture a high-dimensional (usually 3D) data-cube in a compressed manner. Various SCI systems have been built in recent years to capture data such as high-speed videos, hyperspectral images, and the state-of-the-art reconstruction is obtained by the deep neural networks. However, most of these networks are trained in an end-to-end manner by a large amount of corpus with sometimes simulated ground truth, measurement pairs. In this paper, inspired by the untrained neural networks such as deep image priors (DIP) and deep decoders, we develop a framework by integrating DIP into the plug-and-play regime, leading to a self-supervised network for spectral SCI reconstruction. Extensive synthetic and real data results show that the proposed algorithm without training is capable of achieving competitive results to the training based networks. Furthermore, by integrating the proposed method with a pre-trained deep denoising prior, we have achieved state-of-the-art results. {Our code is available at \url{https://github.com/mengziyi64/CASSI-Self-Supervised}.}