计算机工程 ›› 2013, Vol. 39 ›› Issue (7): 284-287.doi: 10.3969/j.issn.1000-3428.2013.07.063

• 图形图像处理 • 上一篇    下一篇

基于聚类的单帧图像超分辨率重建方法

李娟娟,李小红   

  1. (合肥工业大学计算机与信息学院,合肥 230009)
  • 收稿日期:2012-07-23 出版日期:2013-07-15 发布日期:2013-07-12
  • 作者简介:李娟娟(1987-),女,硕士研究生,主研方向:图像处理;李小红,副教授、硕士

Super-resolution Reconstruction Method for Single Frame Image Based on Clustering

LI Juan-juan, LI Xiao-hong   

  1. (School of Computer and Information, Hefei University of Technology, Hefei 230009, China)
  • Received:2012-07-23 Online:2013-07-15 Published:2013-07-12

摘要: 为解决单幅图像的超分辨重建问题,提出一种基于聚类的单帧图像超分辨率重建方法。从高分辨率样本图像中学习一个结构聚类型的高分辨率字典,利用迭代收缩算法优化目标方程,求得高分辨率图像的表示系数,使用学习到的高分辨率字典对低分辨率图像进行重构。实验结果表明,与总变分方法、软切割方法和稀疏表示方法相比,该方法的单帧图像超分辨率重建效果较好。

关键词: 超分辨率, 稀疏表示, 重建方法, 聚类, 字典, 迭代

Abstract: To the question of single frame image super-resolution, implement single frame image super-resolution with the prior of training images, this paper proposes a single frame image super-resolution reconstruction method based on clustering. It builds a structural clustering based high-resolution dictionary from a set of high-resolution images, optimizes objective equation by using iterative shrinkage solution to solve the representation coefficient of high-resolution image, reconstructs low-resolution image by exploiting the learned high-resolution dictionary. Experimental results show that compared with Total Variation(TV) method, Softcuts method and Sparse Representation(SR) method, the effect of the single frame image super-resolution reconstruction of this method is better.

Key words: super-resolution, sparse representation, reconstruction method, clustering, dictionary, iteration

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