In order to solve the problem that super-resolution reconstruction of multi-spectral remote sensing images is susceptible to noise and chromatic aberration, a dark channel and cross channel based multi-prior combined multi-spectral super-resolution algorithm is proposed. First, dark channel prior and cross channel prior are introduced on the basis of traditional total variational prior. Then, based on the maximum posterior probability estimation theory, a multi-spectral super-resolution reconstruction algorithm with multi-prior combination is established. The proposed algorithm can achieve image edge information restoration, image texture information restoration, noise suppression, step effect suppression and chromatic aberration suppression, which can comprehensively improve the quality of reconstructed images. Finally, the experimental verification is carried out, and the results show that the reconstruction effect of the proposed algorithm is significantly improved compared with the existing algorithms under different signal-to-noise ratios (10--40 dB) and chromatic aberrations.