School of Electronic Engineering, Xidian University, Xi’an 710071, China.
IEEE Trans Image Process. 2012 Nov;21(11):4544-56. doi: 10.1109/TIP.2012.2208977. Epub 2012 Jul 16.
Image super-resolution (SR) reconstruction is essentially an ill-posed problem, so it is important to design an effective prior. For this purpose, we propose a novel image SR method by learning both non-local and local regularization priors from a given low-resolution image. The non-local prior takes advantage of the redundancy of similar patches in natural images, while the local prior assumes that a target pixel can be estimated by a weighted average of its neighbors. Based on the above considerations, we utilize the non-local means filter to learn a non-local prior and the steering kernel regression to learn a local prior. By assembling the two complementary regularization terms, we propose a maximum a posteriori probability framework for SR recovery. Thorough experimental results suggest that the proposed SR method can reconstruct higher quality results both quantitatively and perceptually.
图像超分辨率 (SR) 重建本质上是一个不适定的问题，因此设计有效的先验知识非常重要。为此，我们提出了一种新的图像 SR 方法，通过从给定的低分辨率图像中学习非局部和局部正则化先验知识。非局部先验利用了自然图像中相似补丁的冗余性，而局部先验假设一个目标像素可以通过其邻居的加权平均来估计。基于上述考虑，我们利用非局部均值滤波器来学习非局部先验知识，利用导向核回归来学习局部先验知识。通过组合这两个互补的正则化项，我们提出了一种用于 SR 恢复的最大后验概率框架。详尽的实验结果表明，所提出的 SR 方法在定量和感知上都可以重建出更高质量的结果。
