Motivated by the fact that image patch can be sparse represented using a suitable over-complete dictionary

a nephogram super-resolution algorithm via sparse representation using over-complete dictionary is presented. During the experiment two dictionaries Dl and Dh for the low-resolution and high-resolution nephogram patches were trained jointly in order to guarantee that the low-resolution and high-resolution patch pair possesses similar sparse representations as to their own dictionary. Through solving optimization problem

the sparse representation for each low-resolution nephogram patch with respect to Dl was obtained

and the representation coefficients were applied to Dh in order to generate the corresponding high-resolution nephogram patch. At the end of experiment the high-resolution nephogram which satisfies the reconstruction constraint was achieved by using gradient descent algorithm. Numerical experiments for infrared and visual nephogram demonstrate the effectiveness of the proposed algorithm. Moreover

the proposed algorithm outperforms interpolation based methods in terms of visual quality and the Peak Signal to Noise Ratio （PSNR）.