Algorithm of Spectral Super-resolution of Hyperspectral Imagery Based on Redundant Dictionary

To enhance the spatial resolution of hyperspectral image,a hyperspectral image superresolution restoration algorithm based on redundant dictionary was presented in this paper.By training a group of high and low resolution redundant dictionary,the corresponding image element curve of high and low resolution was made to have the same sparse representation coefficients in sparse decomposition based on redundant dictionary in this algorithm.During the process of super-resolution restoration,the low resolution of hyperspectral image sparse decomposes based on low resolution redundant dictionary.The high resolution image was reconstructed by using the sparse representation coefficients and the high resolution dictionary.The experimental results show that,compared with the image patch based sparse super resolution algorithm and the traditional image bilinear interpolation method,the PSNR of image reconstruction is significantly enhanced.The algorithm sparse decomposes the hyperspectral image along the spectral dimension to avoid the traditional algorithm problem of spectral distortion caused by restoration.The computational complexity of the algorithm is significantly reduced.