基于层次聚类的图像超分辨率重建  

Image Super-Resolution Reconstruction Based on Hierarchical Clustering

作　　者：曾台英[1] 杜菲 Zeng Taiying;Du Fei(College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai 200093, Chin)

机构地区：[1]上海理工大学出版印刷与艺术设计学院,上海200093

出　　处：《光学学报》2018年第4期122-129,共8页Acta Optica Sinica

基　　金：上海市教委重点学科资助项目(J50503)

摘　　要：多字典学习的图像超分辨率重建过程中常见的K均值聚类、高斯混合模型聚类等方法会导致图像的重建质量欠佳且不稳定,针对这一问题提出一种新的基于层次聚类的图像超分辨率重建算法;首先对样本图像块提取特征并进行层次聚类,经改进的主成分分析方法训练得到K个字典,然后将测试图像裁切成若干图像块,并分别自适应匹配最合适的字典进行图像块重建,最后对整幅图像进行优化,以实现全局重建。结果表明:所提算法具有较高的可行性,能有效改善图像的重建质量;与传统算法相比,所提算法重建图像的峰值信噪比和结构相似度均有所增大。During image super-resolution reconstruction for multi-dictionary learning,common methods such as K-means clustering,Gauss mixed model clustering and so on can lead to poor quality and instability of image reconstruction.To solve the problem,we propose a novel image super-resolution reconstruction algorithm based on hierarchical clustering.Firstly,features are extracted from sample image blocks,and hierarchical clustering is performed,then K dictionaries are trained with improved principal component analysis method.Secondly,the test images are cut into a number of image blocks,and the most suitable dictionary is adaptively matched to reconstruct the image block.Finally,the whole image is optimized to achieve global reconstruction.The results show that the proposed algorithm in this paper has high feasibility,and can effectively improve the reconstruction quality of image.Compared with peak signal-to-noise ratio and structural similarity of the images reconstructed by the traditional algorithms,those of the images reconstructed by the proposed algorithm increase.

关 键 词：图像处理 图像重建 层次聚类 超分辨率 多字典 稀疏表示 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]