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.

Multispectral imaging system is of wide application in relevant fields for its capability in acquiring spectral information of scenes. Its limitation is that, due to the large number of spectral channels, the imaging process can be quite time-consuming when capturing high-resolution (HR) multispectral images. To resolve this limitation, this paper proposes a fast multispectral imaging framework based on the image sensor pixel-binning and spectral unmixing techniques. The framework comprises a fast imaging stage and a computational reconstruction stage. In the imaging stage, only a few spectral images are acquired in HR, while most spectral images are acquired in low resolution (LR). The LR images are captured by applying pixel binning on the image sensor, such that the exposure time can be greatly reduced. In the reconstruction stage, an optimal number of basis spectra are computed and the signal-dependent noise statistics are estimated. Then the unknown HR images are efficiently reconstructed by solving a closed-form cost function that models the spatial and spectral degradations. The effectiveness of the proposed framework is evaluated using real-scene multispectral images. Experimental results validate that, in general, the method outperforms the state of the arts in terms of reconstruction accuracy, with additional 20× or more improvement in computational efficiency.