光学学报, 2018, 38 (4): 0410004, 网络出版: 2018-07-10   

基于层次聚类的图像超分辨率重建 下载: 918次

Image Super-Resolution Reconstruction Based on Hierarchical Clustering
作者单位
上海理工大学出版印刷与艺术设计学院, 上海 200093
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。
Abstract
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.

曾台英, 杜菲. 基于层次聚类的图像超分辨率重建[J]. 光学学报, 2018, 38(4): 0410004. Taiying Zeng, Fei Du. Image Super-Resolution Reconstruction Based on Hierarchical Clustering[J]. Acta Optica Sinica, 2018, 38(4): 0410004.

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