计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 188-191.

• 模式识别与图像处理 • 上一篇    下一篇

一种自适应稀疏表示和非局部自相似性的图像超分辨率重建算法

张福旺, 苑会娟   

  1. 哈尔滨理工大学测控技术与通信工程学院 哈尔滨150080
  • 出版日期:2019-06-14 发布日期:2019-07-02
  • 通讯作者: 苑会娟(1963-),女,教授,硕士生导师,主要研究方向为传感技术、3D测量技术,E-mail:huijuany@126.com(通信作者)。
  • 作者简介:张福旺(1990-),男,硕士生,主要研究方向为计算机视觉、自然语言处理、数理统计,E-mail:3537064032@qq.com;
  • 基金资助:
    本文受黑龙江省自然科学基金项目(F201303)资助。

Image Super-resolution Reconstruction Algorithm with Adaptive Sparse Representationand Non-local Self-similarity

ZHANG Fu-wang, YUAN Hui-juan   

  1. School of Measurement-Control Technology and Communications Engineering,Harbin University of ;
    Science and Technology,Harbin 150080,China
  • Online:2019-06-14 Published:2019-07-02

摘要: 如何充分利用图像自身蕴含的信息进行超分辨率重建仍然是一个开放的问题。文中提出了一种自适应稀疏表示和非局部自相似性的图像超分辨率重建算法。在训练与重建的过程中都采用K-means算法对选取的数据集进行聚类,将相似的图像块聚集在一起,然后运用PCA处理自适应地选择字典来进行超分辨率重建。相比于通过固定字典进行图像重建,采用自适应选择字典对图像进行重建将使得到的重建图像效果更加优越。针对自然图像的实验结果表明,利用所提算法重建的超分辨率图像的细节更细腻,伪像更少,边缘更锐利。

关键词: 超分辨率, 迭代收缩算法, 非局部自相似性, 稀疏表示

Abstract: How to make full use of the information contained in the image for super-resolution reconstruction is still an open question.This paper proposed an image super-resolution reconstruction algorithm based on adaptive sparse representation and non-local self-similarity.In the process of training and reconstruction,the K-means algorithm is used to cluster the selected datasets,and similar image blocks are gathered together.Then PCA is used to process the adaptive selection dictionary for super-resolution reconstruction.Compared with image reconstruction through a fixed dictionary,the adaptive selection dictionary is used to reconstruct the image,and the effect of reconstructed image obtained will be more superior.The experimental results on natural images show that the super-resolution images reconstructed by the proposed algorithm are more detailed,with fewer artifacts and sharper edges.

Key words: Iterative shrinkage algorithm, Non-local self-similarity, Sparse representation, Super-resolution

中图分类号: 

  • TP391.9
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