Zhu Yu, Zhang Yanning, Yuille Alan L
School of Computer Science, Northwestern Polytechnical University, China.
Department of Statistics, UCLA, USA.
Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2014 Jun;2014:2917-2924. doi: 10.1109/CVPR.2014.373.
We proposed a deformable patches based method for single image super-resolution. By the concept of deformation, a patch is not regarded as a fixed vector but a flexible deformation flow. Via deformable patches, the dictionary can cover more patterns that do not appear, thus becoming more expressive. We present the energy function with slow, smooth and flexible prior for deformation model. During example-based super-resolution, we develop the deformation similarity based on the minimized energy function for basic patch matching. For robustness, we utilize multiple deformed patches combination for the final reconstruction. Experiments evaluate the deformation effectiveness and super-resolution performance, showing that the deformable patches help improve the representation accuracy and perform better than the state-of-art methods.
我们提出了一种基于可变形补丁的单图像超分辨率方法。通过变形的概念,一个补丁不再被视为一个固定向量,而是一个灵活的变形流。通过可变形补丁,字典可以涵盖更多未出现的模式,从而变得更具表现力。我们为变形模型提出了具有缓慢、平滑和灵活先验的能量函数。在基于示例的超分辨率过程中,我们基于最小化能量函数开发变形相似度用于基本补丁匹配。为了提高鲁棒性,我们利用多个变形补丁组合进行最终重建。实验评估了变形效果和超分辨率性能,结果表明可变形补丁有助于提高表示精度,并且比现有方法表现更好。