A new method for super-resolution reconstruction based on the Gaussian-kernel is presented. Each pixel is modeled as a
Gaussian distribution to reconstruct, which is iterated by the image weighting parameter adaptively. The parallelism of
this real-valued algorithm based on the grid model enables better integration of the information of the low-resolution
images of the same scene. Compared to the bi-cubic interpolation algorithm, experiments show that the proposed
algorithm can achieve a gain up over 1.0dB. The visual quality of presented algorithm demonstrate the recovery of
spatial frequencies above the band-limit and corresponding reduction in ringing artifacts when compared with the bicubic
interpolation algorithm. And the proposed method gets better objective and subjective quality by preserving the
sharpness of the edges.