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用预训练的RGB模型对光谱图像的超分辨率方法进行比较评估

Navid Shokoohi¹, Abdelhamid N Fsian¹, Jean-Baptiste Thomas^1,2

¹Imagerie et Vision Artificielle (ImViA) Laboratory, Department Informatique, Electronique, Mécanique (IEM), Université Bourgogne Europe, 21000 Dijon, France.

Sensors (Basel, Switzerland)

|January 28, 2026

概括

本研究评估了光谱成像的超分辨率 (SR) 方法,发现虽然一些模型增强了空间细节,但保持光谱精度需要特定领域的培训. 这项研究为光谱图像恢复提供了可重现的基线.

科学领域:

* 光谱成像技术
* * 计算机视觉计算机视觉
* * 图像处理图像处理

背景情况:

* 硬件限制和稀缺的注释数据集限制了光谱成像分辨率.
* 超分辨率 (SR) 技术为增强光谱数据中的空间细节提供了一个潜在的解决方案.
*评估各种SR方法对于推进光谱图像恢复至关重要.

研究的目的:

*全面评估基于插值,基于CNN,基于GAN和基于扩散的SR方法用于光谱成像.
* 建立一个可重复的框架来评估在光谱数据上的SR性能.
* 在SR模型中识别空间增强和光谱保真之间的权衡.

主要方法:

* 开发一个合成的30频谱数据集,使用MST++进行基准真相.
*为了兼容性,在现有的SR架构中输入非相邻的RGB三重体.
*评估SR模型 (双立方,CNN,ESRGAN,扩散模型) 在×2,×4和×8尺度使用PSNR,SSIM和SAM指标.

主要成果:

*双立方插值作为一个光谱可靠的基线.
* 浅浅的CNN可以在没有微调的情况下表现出良好的概括性.
*ESRGAN增强了空间细节,但损害了光谱精度.
*扩散模型在没有光谱域适应的情况下显示不稳定的性能,需要光谱意识培训.

结论:

* 在SR中,感知敏度和光谱忠实度之间存在着持久的权衡.
*对于用于光谱数据的生成式SR模型,域意识目标至关重要.
*这项研究提供了可重现的基线和未来光谱图像恢复研究的评估框架.

关键词:

从RGB到光谱的重建扩散模型的扩散模型生成式的图像修复恢复.超光谱成像技术的使用.频谱过器阵列 (SFA) 的使用.频谱成像技术的使用.超分辨率 (SR) 超分辨率 (SR) 是指超分辨率的超分辨率.

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