论文标题
合奏学习先验正在展开可扩展快照压缩感应
Ensemble learning priors unfolding for scalable Snapshot Compressive Sensing
论文作者
论文摘要
快照压缩成像（SCI）可以通过2D测量记录3D信息，并从此2D测量中通过重建算法重建原始的3D信息。如我们所见，重建算法在SCI中起着至关重要的作用。最近，深度学习算法显示出其出色的能力，表现优于传统算法。因此，为了提高深度学习算法的重建精度是SCI的必然主题。此外，深度学习算法通常受到可扩展性的限制，如果缺乏新的培训过程，则训练有素的模型通常无法应用于新系统。为了解决这些问题，我们开发了集合学习先验，以进一步提高重建精度，并提出可扩展的学习，以赋予像传统算法一样深入学习可扩展性的能力。更重要的是，我们的算法达到了最新结果，表现优于现有算法。模拟和实际数据集的广泛结果证明了我们提出的算法的优越性。代码和模型将发布给公众。
Snapshot compressive imaging (SCI) can record the 3D information by a 2D measurement and from this 2D measurement to reconstruct the original 3D information by reconstruction algorithm. As we can see, the reconstruction algorithm plays a vital role in SCI. Recently, deep learning algorithm show its outstanding ability, outperforming the traditional algorithm. Therefore, to improve deep learning algorithm reconstruction accuracy is an inevitable topic for SCI. Besides, deep learning algorithms are usually limited by scalability, and a well trained model in general can not be applied to new systems if lacking the new training process. To address these problems, we develop the ensemble learning priors to further improve the reconstruction accuracy and propose the scalable learning to empower deep learning the scalability just like the traditional algorithm. What's more, our algorithm has achieved the state-of-the-art results, outperforming existing algorithms. Extensive results on both simulation and real datasets demonstrate the superiority of our proposed algorithm. The code and models will be released to the public.
