计算机应用 ›› 2014, Vol. 34 ›› Issue (2): 562-566.

• 虚拟现实与数字媒体 • 上一篇    下一篇

基于改进稀疏编码的图像超分辨率算法

盛帅1,曹丽萍2,黄增喜1,吴鹏飞1   

  1. 1. 四川大学 计算机学院, 成都 610065;
    2. 四川大学 图书馆,成都 610065
  • 收稿日期:2013-06-27 修回日期:2013-09-09 出版日期:2014-02-01 发布日期:2014-03-01
  • 通讯作者: 盛帅
  • 作者简介:黄增喜(1985-),男,广西百色人,博士研究生,主要研究方向:图像处理、生物特征识别;吴鹏飞(1989-),男,江苏常州人,硕士研究生,主要研究方向:机器视觉
  • 基金资助:
    国家自然科学基金资助项目;四川省国际科技合作与交流研究计划项目;四川省科技支撑计划项目

Image super-resolution algorithm based on improved sparse coding

SHENG Shuai1,CAO Liping2,HUANG Zengxi1,WU Pengfei1   

  1. 1. College of Computer Science, Sichuan University, Chengdu Sichuan 610065, China;
    2. Library, Sichuan University, Chengdu Sichuan 610065, China
  • Received:2013-06-27 Revised:2013-09-09 Online:2014-02-01 Published:2014-03-01
  • Contact: SHENG Shuai

摘要: 针对传统基于稀疏字典对的超分辨率(SR)算法训练速度慢、字典质量差、特征匹配准确性低的缺点,提出一种基于改进稀疏编码的图像超分辨率算法。该算法使用自适应阈值的形态组成分析(MCA)方法提取图像特征,并采用主成分分析算法对训练集进行降维,提高特征提取的有效性,缩短字典训练时间,减少过拟合现象。在字典训练阶段,使用改进的稀疏K-奇异值分解(K-SVD)算法训练低分辨率字典,结合图像块的重叠关系求解高分辨率字典,增强字典的有效性和自适应能力,同时极大地提高了字典的训练速度。在Lab颜色空间对彩色图像进行重建,避免由于颜色通道相关性造成的重建图像质量下降。与传统方法相比,该算法重建图像质量和计算效率更优。

关键词: 超分辨率, 稀疏表示, 形态组成分析, 主成分分析, 颜色空间, 机器学习

Abstract: The traditional Super-Resolution (SR) algorithm, based on sparse dictionary pairs, is slow in training speed, poor in dictionary quality and low in feature matching accuracy. In view of these disadvantages, a super-resolution algorithm based on the improved sparse coding was proposed. In this algorithm, a Morphological Component Analysis (MCA) method with adaptive threshold was used to extract picture feature, and Principal Component Analysis (PCA) algorithm was employed to reduce the dimensionality of training sets. In this way, the effectiveness of the feature extraction was improved, the training time of dictionary was shortened and the over-fitting phenomenon was reduced. An improved sparse K-Singular Value Decomposition (K-SVD) algorithm was adopted to train low-resolution dictionary, and the super-resolution dictionary was solved by utilizing overlapping relation, which enforced the effectiveness and self-adaptability of the dictionary. Meanwhile, the training speed was greatly increased. Through the reconstruction of color images in the Lab color space, the degradation of the reconstructed image quality, which may be caused by the color channel's correlation, was avoided. Compared with traditional methods, this proposed approach can get better high-resolution images and higher computational efficiency.

Key words: super-resolution, sparse representation, Morphological Component Analysis (MCA), Principal Component Analysis (PCA), color space, machine learning

中图分类号: