一种基于正则化技术的超分辨影像重建方法

超分辨率影像重建已经成为近年来人们广泛研究的热点，利用超分辨率重建技术，可以得到分辨率高于原始影像的重建影像。为此，提出了一个利用多幅具有亚像素位移的低分辨率欠采样影像重建一幅高分辨影像的超分辨率重建方法。该方法利用正则化技术，通过迭代运算解求重建影像的最优解。在迭代过程中，得到的重建影像用于求解下一次迭代的正则化参数，不断的循环迭代，最后求解出重建影像的最优解。对Lena影像进行了处理，并用PSNR影像评价方法对重建影像进行了定量评价。实验结果证明，该方法能较大限度地减弱噪声对重建结果的影响，当重建比率较大时，仍可得到高质量的高分辨率重建影像。

A Regularized Super-resolution Image Reconstruction Method

Super-resolution image reconstruction has been one of the most active research areas in recent years. In this paper, a super-resolution solution is proposed to the problem of obtaining a high resolution image from several low resolution images that have been subsampled and displaced by different amounts of sub-pixel shifts. The method is based on the regularization technique, solving the constrained optimization by proposed iteration steps. At each iteration step, the regularization parameter is determined using the partially reconstructed image solved at the last step. The proposed algorithm is tested on synthetic images,and the reconstructed images are evaluated by a PSNR method. The results indicate that the proposed algorithm has considerable effectiveness in terms of both objective measurements and visual evaluation.