Gaussian kernel-based variable-grid image super-resolution reconstruction
Cheng Zhou, Yihua Tan, Jinwen Tian, Wenpo Ma · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
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.