融合特征分类和独立字典训练的超分辨率重建

摘要

超分辨率重建技术在重构图像细节、改善图像视觉效果方面具有重要作用.为进一步提高图像的重建质量,提出了一种有效的超分辨率重建方法.首先提取图像块的几何特征来构造决策树,以期通过监督的方式进行图像块分类.然后针对不同类型的图像块训练集,分别基于K-SVD独立训练相应的高分辨率字典和低分辨率字典.最后为了保证图像块的准确和快速重建,对高分辨率训练集和低分辨率训练集的系数求解映射矩阵,其用于在重建阶段将低分辨率稀疏系数映射为高分辨率稀疏系数以达到重建目的.实验结果表明,本文的方法与其他经典的超分辨率重建方法相比,在重建效果方面具有明显提高.%Super-resolution reconstruction plays an important role in reconstructing image detail and improving im-age visual effects. A new effective super-resolution method is proposed. Firstly, we extract the geometric features of the image patch to construct the decision tree, which will be used in patch classification in a supervised way. Then, we train the high-resolution and low-resolution dictionaries based on K-SVD independently for different types of training sets. Finally, we solve the mapping matrix for the coefficients between the high-resolution and low-reso-lution training set, which are used to map the low-resolution coefficients to high-resolution coefficients during the re-construction phase to ensure accurate and fast reconstruction of the image patches. The experimental results show that the proposed method has a significant improvement in the reconstruction effect compared with other classic methods.