论文标题
一种基于稀疏性和深层图像先验的编码光圈快照光谱成像的快速交流最小化算法
A Fast Alternating Minimization Algorithm for Coded Aperture Snapshot Spectral Imaging Based on Sparsity and Deep Image Priors
论文作者
论文摘要
编码的光圈快照光谱成像（CASSI）是一种用于从一个或几个二维投影测量值重建三维高光谱图像（HSI）的技术。但是，较少的投影测量或更多的光谱通道会导致一个严重的问题，在这种情况下，必须应用正则化方法。为了显着提高重建的准确性，本文提出了一种基于自然图像的稀疏性和深层图像先验（FAMA-SDIP）的快速交替最小化算法。通过将深层图像（DIP）整合到压缩感应（CS）重建原理中，所提出的算法可以实现最新的结果，而无需任何培训数据集。广泛的实验表明，FAMA-SDIP方法显着优于模拟和实际HSI数据集的主要主要方法。
Coded aperture snapshot spectral imaging (CASSI) is a technique used to reconstruct three-dimensional hyperspectral images (HSIs) from one or several two-dimensional projection measurements. However, fewer projection measurements or more spectral channels leads to a severly ill-posed problem, in which case regularization methods have to be applied. In order to significantly improve the accuracy of reconstruction, this paper proposes a fast alternating minimization algorithm based on the sparsity and deep image priors (Fama-SDIP) of natural images. By integrating deep image prior (DIP) into the principle of compressive sensing (CS) reconstruction, the proposed algorithm can achieve state-of-the-art results without any training dataset. Extensive experiments show that Fama-SDIP method significantly outperforms prevailing leading methods on simulation and real HSI datasets.
