电子学报 ›› 2012, Vol. 40 ›› Issue (5): 920-925.DOI: 10.3969/j.issn.0372-2112.2012.05.010

• 学术论文 • 上一篇    下一篇

基于图像块分类稀疏表示的超分辨率重构算法

练秋生, 张伟   

  1. 燕山大学信息科学与工程学院, 河北秦皇岛 066004
  • 收稿日期:2010-12-20 修回日期:2012-01-05 出版日期:2012-05-25
    • 基金资助:
    • 国家自然科学基金 (No.61071200,No.60772079); 河北省自然科学基金 (No.F2010001294)

Image Super-Resolution Algorithms Based on Sparse Representation of Classified Image Patches

LIAN Qiu-sheng, ZHANG Wei   

  1. Institute of Information Science and Technology, Yanshan University, Qinhuangdao, Hebei 066004, China
  • Received:2010-12-20 Revised:2012-01-05 Online:2012-05-25 Published:2012-05-25

摘要: 目前基于图像块稀疏表示的超分辨率重构算法对所有图像块都用同一字典表示,不能反映不同类型图像块间的差别.针对这一缺点,本文提出基于图像块分类稀疏表示的方法.该方法先利用图像局部特征将图像块分为平滑、边缘和不规则结构三种类型,其中边缘块细分为多个方向.然后利用稀疏表示方法对边缘和不规则结构块分别训练各自对应的低分辨率和高分辨率字典.重构时对平滑块利用简单双三次插值方法,边缘和不规则结构块由其对应的高、低分辨率字典通过正交匹配追踪算法重构.实验结果表明,与单字典稀疏表示算法相比,本文算法对图像边缘部分重构质量明显改善,同时重构速度显著提高.

关键词: 超分辨率, 稀疏表示, 块分类, 正交匹配追踪

Abstract: At present,super-resolution algorithms based on sparse representation of image patches exploit single dictionary to represent the image patches,which can not reflect the differences of various image patches types.In this paper,a novel method based on sparse representation of classified image patches is proposed to overcome this disadvantage.In this method,image patches are firstly divided into smooth patches,different directional edge patches and irregular structure patches by local features.Then these classified patches are applied into training the corresponding high and low resolution dictionary pairs.During the reconstruction process,simple bicubic interpolation approach is used for smooth patches while edge and irregular structure patches are reconstructed from their corresponding dictionary pairs using orthogonal matching pursuit algorithm.Experiment results show that the proposed algorithm significantly improves the visual quality of the edges and has faster speed compared with other single dictionary methods.

Key words: super-resolution, sparse representation, patch classification, orthogonal matching pursuit

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