用于光谱反卷积成像的分层空间-频率聚合方法
Hierarchical Spatial-Frequency Aggregation for Spectral Deconvolution Imaging
AI总结：
针对光谱反卷积成像中数据依赖算子导致的重建难题，本文提出分层空间-频率聚合展开Transformer（HSFAUT），通过频域线性化和空间-频率先验聚合实现高效高保真重建，性能超越现有方法。
摘要：
计算光谱成像（CSI）通过协同设计的光学系统与算法实现实时光谱成像，但典型的CSI方法存在体积庞大和保真度有限的问题。因此，近年来基于点扩散函数（PSF）工程的光谱反卷积成像（SDI）方法被提出，以实现高保真度的紧凑型CSI设计。然而，SDI的复合卷积-积分操作使得法方程系数矩阵依赖于场景，这阻碍了成像先验的有效利用，并对精确重建提出了挑战。为应对SDI中固有的数据依赖算子，我们提出了一种分层空间-光谱聚合展开框架（HSFAUF）。通过分解子问题并将其投影到频域，HSFAUF将非线性过程转化为线性映射，从而实现高效求解。此外，为了在迭代细化过程中整合空间-光谱先验，我们提出了一种空间-频率聚合Transformer（SFAT），它显式地聚合空间域和频率域的信息。通过将SFAT集成到HSFAUF中，我们开发了一种基于Transformer的深度展开方法，即分层空间-频率聚合展开Transformer（HSFAUT），用于求解SDI的逆问题。系统的仿真和真实实验表明，HSFAUT以更低的内存和计算成本超越了当前最优（SOTA）方法，并在不同的SDI系统上展现出最优性能。
英文摘要：
Computational spectral imaging (CSI) achieves real-time hyperspectral imaging through co-designed optics and algorithms, but typical CSI methods suffer from a bulky footprint and limited fidelity. Therefore, Spectral Deconvolution imaging (SDI) methods based on PSF engineering have been proposed to achieve high-fidelity compact CSI design recently. However, the composite convolution-integration operations of SDI render the normal-equation coefficient matrix scene-dependent, which hampers the efficient exploitation of imaging priors and poses challenges for accurate reconstruction. To tackle the inherent data-dependent operators in SDI, we introduce a Hierarchical Spatial-Spectral Aggregation Unfolding Framework (HSFAUF). By decomposing subproblems and projecting them into the frequency domain, HSFAUF transforms nonlinear processes into linear mappings, thereby enabling efficient solutions. Furthermore, to integrate spatial-spectral priors during iterative refinement, we propose a Spatial-Frequency Aggregation Transformer (SFAT), which explicitly aggregates information across spatial and frequency domains. By integrating SFAT into HSFAUF, we develop a Transformer-based deep unfolding method, \textbf{H}ierarchical \textbf{S}patial-\textbf{F}requency \textbf{A}ggregation \textbf{U}nfolding \textbf{T}ransformer (HSFAUT), to solve the inverse problem of SDI. Systematic simulated and real experiments show that HSFAUT surpasses SOTA methods with cheaper memory and computational costs, while exhibiting optimal performance on different SDI systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
