无遮挡光路实现高透光光谱成像，提升弱光下图像质量。
Oscillating Dispersion for Maximal Light-throughput Spectral Imaging
- 通过移动分光元件在共轭与离焦位置间振荡，实现单光路全透光采集。
- 在低光照条件下重建精度超越现有方法，物理原型验证了高质量复原。
- 结合全色图像引导的深度展开网络，精准修正亚像素光谱错位。
现有计算光谱成像系统依赖编码孔径和分束器，会阻挡大量入射光，导致弱光环境下重建质量下降。为此，我们提出振荡分光成像光谱仪（ODIS），首次通过轴向移动分光元件在共轭像面与离焦位置之间切换，沿单一光路依次获取全色（PAN）图像和分光测量数据，实现近似全光通量。进一步提出基于全色引导的分光感知深度展开网络（PDAUN），在无掩模分光条件下恢复高保真光谱信息。其数据保真步利用ODIS前向模型的循环卷积特性，设计基于FFT-Woodbury预条件的求解器；而分光感知可变形卷积模块（DADC）则利用全色特征校正亚像素级光谱错位。实验表明，在标准基准上达到业界领先性能，跨系统对比证实其在低照度下有显著优势。高保真重建已在物理原型上得到验证。
原文摘要 · Abstract (English)
Existing computational spectral imaging systems typically rely on coded aperture and beam splitters that block a substantial fraction of incident light, degrading reconstruction quality under light-starved conditions. To address this limitation, we develop the Oscillating Dispersion Imaging Spectrometer (ODIS), which for the first time achieves near-full light throughput by axially translating a disperser between the conjugate image plane and a defocused position, sequentially capturing a panchromatic (PAN) image and a dispersed measurement along a single optical path. We further propose a PAN-guided Dispersion-Aware Deep Unfolding Network (PDAUN) that recovers high-fidelity spectral information from maskless dispersion under PAN structural guidance. Its data-fidelity step derives an FFT-Woodbury preconditioned solver by exploiting the cyclic-convolution property of the ODIS forward model, while a Dispersion-Aware Deformable Convolution module (DADC) corrects sub-pixel spectral misalignment using PAN features. Experiments show state-of-the-art performance on standard benchmarks, and cross-system comparisons confirm that ODIS yields decisive gains under low illumination. High-fidelity reconstruction is validated on a physical prototype.
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