基于自相似性和稀疏表示的图像超分辨率重建

摘要

图像超分辨率重建技术在提升图像质量，改善图像视觉效果等方面有着重要意义。为了充分利用图像自身蕴含的信息，本文提出一种基于自相似性和稀疏表示的单幅图像超分辨率重建算法。针对图像中存在的相同尺度和不同尺度的相似结构，算法联合稀疏 K-SVD 字典学习方法和非局部均值方法将蕴含在其中的有效信息以正则项的形式加入到最大后验概率估计框架中，然后，采用梯度下降法求解算法构建的目标函数，重建出高分辨率图像。实验表明，与经典的算法相比，本文算法在视觉效果和评价指标上都有一定的提高。%Super-resolution reconstruction plays an important role in adding the image details and improving the visual perception. In order to effectively exploit the effective information hidden in the image itself, we proposed a single image super-resolution reconstruction method based on self-similarity and sparse representation. The method combines sparse K-SVD dictionary learning and nonlocal means,which are used to add the effective information hidden in the same scale and across different scales structural self-similarity into the maximum a posteriori probability estimation framework by two different regularization terms. Then, a local optimal solution is obtained by using the gradient descent algorithm. The experimental results show that our method has a better improvement both visually and quantitatively.