论文标题
使用卷积和上下文变压器的光谱压缩成像重建
Spectral Compressive Imaging Reconstruction Using Convolution and Contextual Transformer
论文作者
论文摘要
光谱压缩成像（SCI）能够将高维高光谱图像编码为2D测量，然后使用算法来重建时空光谱数据处。目前，SCI的主要瓶颈是重建算法，最新的（SOTA）重建方法通常会面临长期重建时间和/或细节恢复不良的问题。在本文中，我们提出了一个新型的混合网络模块，即CCOT（卷积和上下文变压器）块，该模块可以同时获得卷积的感应偏置能力和强大的变压器建模能力，并有助于提高重建质量以恢复细节。我们将提出的CCOT块集成到基于广义交替投影算法的深层展开框架中，并进一步提出GAP-CCOT网络。通过大量合成和真实数据的实验，我们提出的模型可实现更高的重建质量（$> $> $> $> $ 2DB的PSNR在模拟基准数据集中）和比现有SOTA算法的运行时间更短。代码和模型可在https://github.com/ucaswangls/gap-ccot上公开获得。
Spectral compressive imaging (SCI) is able to encode the high-dimensional hyperspectral image to a 2D measurement, and then uses algorithms to reconstruct the spatio-spectral data-cube. At present, the main bottleneck of SCI is the reconstruction algorithm, and the state-of-the-art (SOTA) reconstruction methods generally face the problem of long reconstruction time and/or poor detail recovery. In this paper, we propose a novel hybrid network module, namely CCoT (Convolution and Contextual Transformer) block, which can acquire the inductive bias ability of convolution and the powerful modeling ability of transformer simultaneously,and is conducive to improving the quality of reconstruction to restore fine details. We integrate the proposed CCoT block into deep unfolding framework based on the generalized alternating projection algorithm, and further propose the GAP-CCoT network. Through the experiments of extensive synthetic and real data, our proposed model achieves higher reconstruction quality ($>$2dB in PSNR on simulated benchmark datasets) and shorter running time than existing SOTA algorithms by a large margin. The code and models are publicly available at https://github.com/ucaswangls/GAP-CCoT.
