自适应邻域选取的邻域嵌入超分辨率重建算法

在邻域嵌入超分辨率重建算法中，训练和重建过程均在特征空间进行的，因此，特征选择对算法的性能具有较大的影响。另外，大多数基于邻域嵌入算法对训练得到的样本库未经测试直接使用，使得邻域选择具有“盲目”性。考虑到特征选择的重要性以及避免邻域选择的盲目性，本文提出了一种新的邻域嵌入超分辨率重建算法。第一步:利用专家矢量场模型估计出输入图像的全局图像；第二步:利用邻域嵌入算法重建残差图像。在重建残差图像的过程中，首先将图像分成若干子块并利用线性滤波器提取特征；然后，将训练图像分成两组，第一组训练得到高、低分辨率重建样本库，第二组对重建样本库测试，得到邻域选择库；最后，自适应的选择输入图像子块的邻域数目，并利用重建样本库重建。仿真实验结果表明，相比其他基于邻域嵌入算法，提出算法可以重建更多的细节信息和锐利的边缘，重建得到的高分辨率图像具有较高的主客观质量。 

Super resolution through neighbor embedding with adaptive neighbor selection

In super resolution through neighbor embedding (NE), the training and reconstruction process were performed in the feature space, and so the feature selection is very important. In addition, most NE-based methods use the dictionary without test, which make the choice of neighbors blindly. Considering the importance of feature selection and to avoid the blindness of neighbor selection, we proposed a novel super resolution through neighbor embedding approach. Firstly, estimate the global image with field of experts. Secondly, recover the residual image through NE-based method. In the process of residual image reconstruction, we firstly divide the image into patches and extract the feature with linear filter. Then, the training images are divided into two groups. One group is used to train the low and high resolution reconstruction dictionary, and the other is used to test the reconstruction dictionary and obtain the neighbor selection dictionary. Finally, we select the neighborhood size for the input image patches and reconstruct them with reconstruction dictionary. The experiment results show that, compared with other NE-based methods, our method can recover more high frequency details and sharp edge. What’s more, the recover images have better subjective and objective quality.