Multispectral Image Super-Resolution via RGB Image Fusion and Radiometric Calibration

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.