Learned optics, which incorporate lightweight diffractive optics, coded-aperture modulation, and specialized image-processing neural networks, have recently garnered attention in the field of snapshot hyperspectral imaging (HSI). While conventional methods typically rely on a single lens element paired with an off-the-shelf color sensor, these setups, despite their widespread availability, present inherent limitations. First, the Bayer sensor's spectral response curves are not optimized for HSI applications, limiting spectral fidelity of the reconstruction. Second, single lens designs rely on a single diffractive optical element (DOE) to simultaneously encode spectral information and maintain spatial resolution across all wavelengths, which constrains spectral encoding capabilities. This work investigates a multi-channel lens array combined with aperture-wise color filters, all co-optimized alongside an image reconstruction network. This configuration enables independent spatial encoding and spectral response for each channel, improving optical encoding across both spatial and spectral dimensions. Specifically, we validate that the method achieves over a 5dB improvement in PSNR for spectral reconstruction compared to existing single-diffractive lens and coded-aperture techniques. Experimental validation further confirmed that the method is capable of recovering up to 31 spectral bands within the 429--700 nm range in diverse indoor and outdoor environments.

中文翻译：

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用于快照高光谱成像的学习多孔径彩色编码光学器件


结合轻量级衍射光学元件、编码孔径调制和专用图像处理神经网络的学习光学器件最近在快照高光谱成像 （HSI） 领域引起了关注。虽然传统方法通常依赖于单个镜头元件与现成的颜色传感器配对，但这些设置尽管广泛使用，但存在固有的局限性。首先，拜耳传感器的光谱响应曲线没有针对 HSI 应用进行优化，限制了重建的光谱保真度。其次，单透镜设计依赖于单个衍射光学元件 （DOE） 来同时编码光谱信息并保持所有波长的空间分辨率，这限制了光谱编码能力。这项工作研究了多通道镜头阵列与孔径彩色滤光片相结合，所有这些都与图像重建网络一起进行了协同优化。这种配置支持每个通道的独立空间编码和光谱响应，从而改善了空间和光谱维度的光学编码。具体来说，我们验证了与现有的单衍射透镜和编码孔径技术相比，该方法在光谱重建中的 PSNR 提高了 5dB 以上。实验验证进一步证实，该方法能够在不同的室内和室外环境中恢复 429--700 nm 范围内的多达 31 个光谱波段。