IEEE Trans Image Process. 2019 Apr;28(4):1783-1797. doi: 10.1109/TIP.2018.2881911. Epub 2018 Nov 26.
Multispectral imaging is of wide application for its capability in acquiring the spectral information of scenes. Due to hardware limitation, multispectral imaging device usually cannot achieve high-spatial resolution. To address the issue, this paper proposes a multispectral image super-resolution algorithm, referred as SRIF, by fusing the low-resolution multispectral image and the high-resolution (HR) RGB image. It deals with the general circumstance that image intensity is linear to scene radiance for multispectral imaging devices while is nonlinear and unknown for most RGB cameras. The SRIF algorithm first solves the inverse camera response function and a spectral sensitivity function of RGB camera, and establishes the linear relationship between multispectral and RGB images. Then the unknown HR multispectral image is efficiently reconstructed according to the linear image degradation models. Meanwhile, the edge structure of the reconstructed HR multispectral image is kept in accordance with that of the RGB image using a weighted total variation regularizer. The effectiveness of the SRIF algorithm is evaluated on both public datasets and our image set. Experimental results validate that the SRIF algorithm outperforms the state-of-the-arts in terms of both reconstruction accuracy and computational efficiency.
多光谱成像是一种广泛应用的技术，因为它能够获取场景的光谱信息。由于硬件限制，多光谱成像设备通常无法实现高空间分辨率。为了解决这个问题，本文提出了一种多光谱图像超分辨率算法，称为 SRIF，通过融合低分辨率多光谱图像和高分辨率 (HR) RGB 图像来实现。它处理了多光谱成像设备中图像强度与场景辐射呈线性关系，而大多数 RGB 相机中图像强度是非线性且未知的一般情况。SRIF 算法首先求解 RGB 相机的逆相机响应函数和光谱灵敏度函数，并建立多光谱图像和 RGB 图像之间的线性关系。然后，根据线性图像降质模型，有效地重建未知的 HR 多光谱图像。同时，使用加权全变差正则化器保持重建的 HR 多光谱图像的边缘结构与 RGB 图像的边缘结构一致。在公共数据集和我们的图像集上评估了 SRIF 算法的有效性。实验结果验证了 SRIF 算法在重建精度和计算效率方面均优于现有技术。
