统一最小二乘规则的单幅图像超分辨算法

doi:10.11772/j.issn.1001-9081.2016.03.800

计算机应用 ›› 2016, Vol. 36 ›› Issue (3): 800-805.DOI: 10.11772/j.issn.1001-9081.2016.03.800

统一最小二乘规则的单幅图像超分辨算法

赵小乐¹, 吴亚东¹, 田金沙¹, 张红英²

1. 西南科技大学计算机科学与技术学院, 四川绵阳 621010;
2. 西南科技大学信息工程学院, 四川绵阳 621010

收稿日期:2015-08-12 修回日期:2015-10-04 出版日期:2016-03-10 发布日期:2016-03-17
通讯作者: 吴亚东
作者简介:赵小乐(1987-),男,四川南部人,硕士研究生,CCF会员,主要研究方向:数字图像处理;吴亚东(1979-),男,河南周口人,教授,博士,CCF会员,主要研究方向:数字图像处理、信息可视化、人机交互;田金沙(1988-),女,河北衡水人,硕士研究生,主要研究方向:数字图像处理;张红英(1976-),女,四川德阳人,教授,博士,主要研究方向:数字图像处理。
基金资助:
国家自然科学基金资助项目(61303127);四川省科技支撑计划项目(2014SZ0223);四川省教育厅重点项目(13ZA0169);中国科学院"西部之光"人才培养计划项目(13ZS0106);西南科技大学创新基金资助项目(15ycx053)。

Single image super-resolution algorithm based on unified iterative least squares regulation

ZHAO Xiaole¹, WU Yadong¹, TIAN Jinsha¹, ZHANG Hongying²

1. School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang Sichuan 621010, China;
2. School of Information engineering, Southwest University of Science and Technology, Mianyang Sichuan 621010, China

Received:2015-08-12 Revised:2015-10-04 Online:2016-03-10 Published:2016-03-17
Supported by:
This work was supported by the Natural Science Foundation of China (61303127), Sichuan Science and Technology Support Program (2014SZ0223), the Major Project of Education Office of Sichuan Province (13ZA0169), and the "West Light Training" Project of Chinese Academy of Sciences (13ZS0106), the Innovation Fund Project of Southwest University of Science and Technology (15ycx053).

摘要/Abstract

摘要： 基于机器学习的超分辨方法是一个很有发展前景的单幅图像超分辨方法,稀疏表达和字典学习是其中的研究热点。针对比较耗时的字典训练与恢复精度不高图像重建,从减小低分辨率(LR)和高分辨率(HR)特征空间之间差异性的角度提出了一种使用迭代最小二乘字典学习算法(ILS-DLA),并使用锚定邻域回归(ANR)进行图像重建的单幅图像超分辨算法。迭代最小二乘法的整体优化过程极大地缩短了低分辨字典/高分辨字典的训练时间,它采用了与锚定邻域回归相同的优化规则,有效地保证了字典学习和图像重建在理论上的一致性。实验结果表明,所提算法的字典学习效果比K-均值奇异值分解(K-SVD)和Beta过程联合字典学习(BPJDL)等算法更高效,图像重建的效果也优于许多优秀的超分辨算法。

关键词: 迭代最小二乘法, 锚定邻域回归, 稀疏表达, 字典学习, 超分辨

Abstract: Machine learning based image Super-Resolution (SR) has been proved to be a promising single-image SR technology, in which sparseness representation and dictionary learning has become the hotspot. Aiming at the time-consuming dictionary training and low-accuracy SR recovery, an SR algorithm was proposed from the perspective of reducing the inconsistency between Low-Resolution (LR) feature and High-Resolution (HR) feature spaces as far as possible. The authors adopted Iterative Least Squares Dictionary Learning Algorithm (ILS-DLA) to train LR/HR dictionaries and Anchored Neighborhood Regression (ANR) to recover HR images. ILS-DLA was able to train LR/HR dictionaries in relatively short time because of its integral optimization procedure, by adopting the same optimization strategy of ANR, which theoretically reduced the diversity between LR/HR dictionaries effectively. A large number of experiments show that the proposed method achieves superior dictionary learning to K-means Singular Value Decomposition (K-SVD) and Beta Process Joint Dictionary Learning (BPJDL) algorithms etc., and provides better image restoration results than other state-of-the-art SR algorithms.

Key words: Iterative Least Squares (ILS), Anchored Neighborhood Regression (ANR), Sparseness Representation (SR), dictionary learning, super-resolution

中图分类号:

TP391.41

赵小乐, 吴亚东, 田金沙, 张红英. 统一最小二乘规则的单幅图像超分辨算法[J]. 计算机应用, 2016, 36(3): 800-805.

ZHAO Xiaole, WU Yadong, TIAN Jinsha, ZHANG Hongying. Single image super-resolution algorithm based on unified iterative least squares regulation[J]. Journal of Computer Applications, 2016, 36(3): 800-805.

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统一最小二乘规则的单幅图像超分辨算法

Single image super-resolution algorithm based on unified iterative least squares regulation

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