Noise face hallucination via data-driven local eigentransformation

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (12): 3576-3579.

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Noise face hallucination via data-driven local eigentransformation

DONG Xiaohui¹,²,GAO Ge¹,²,CHEN Liang¹,²,HAN Zhen¹,²,JIANG Junjun¹,²

1. National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan Hubei 430072, China;
2. School of Computer, Wuhan University, Wuhan Hubei 430072, China

Received:2014-06-17 Revised:2014-08-08 Online:2014-12-31 Published:2014-12-01
Contact: DONG Xiaohui

数据驱动局部特征转换的噪声人脸幻构

董小慧¹,²,高戈¹,²,陈亮¹,²,韩镇¹,²,江俊君¹,²

1. 武汉大学国家多媒体工程技术研究中心,武汉 430072
2. 武汉大学计算机学院,武汉 430072

通讯作者: 董小慧
作者简介:董小慧(1988-）,女,湖北黄冈人,硕士研究生,主要研究方向:视频图像处理；高戈(1973-）,男,湖北武汉人,副教授,博士,主要研究方向:音视频信号编码与处理；陈亮(1988-）,女,河南南阳人,博士研究生,主要研究方向:图像处理、模式识别；韩镇(1980-）,男,湖北十堰人,讲师,博士,主要研究方向:视频图像编码与处理；江俊君(1986-）,男,湖北大冶人,博士研究生,主要研究方向:视频图像处理。
基金资助:
国家自然科学基金资助重点项目;国家自然科学基金资助项目;国家自然科学基金资助项目;中国博士后科学基金;湖北省重大科技创新计划;粤港关键领域重点突破项目;教育部高校博士点基金新教师类项目;武汉市科技攻关计划项目

Abstract

Abstract:

Concerning the problem that the linear eigentransformation method cannot capture the statistical properties of the nonlinear facial image, a Data-driven Local Eigentransformation (DLE) method for face hallucination was proposed. Firstly, some samples most similar to the input image patch were searched. Secondly, a patch-based eigentransformation method was used for modeling the relationship between the Low-Resolution (LR) and High-Resolution (HR) training samples. Finally, a post-processing approach refined the hallucinated results. The experimental results show the proposed method has better visual performance as well as 1.81dB promotion over method of locality-constrained representation in objective evaluation criterion for face image especially with noise. This method can effectively hallucinate surveillant facial images.

摘要：

针对线性主成分分析方法无法有效揭示非线性人脸图像统计特性的问题,提出一种基于数据驱动局部特征转换的(DLE)人脸幻构。首先,选取与输入图像块最相似的训练样本块；然后,利用基于图像块的特征转换算法对高低分辨率训练样本块间的关系进行建模；最后,利用后处理进程对重建结果进行优化。实验结果表明所提算法对低分辨率图像,尤其是含噪声人脸图像,有更好的主观重建效果,在客观评价指标上比局部约束表示方法提高1.81dB。该算法能有效实现监控人脸图像超分辨率增强。

CLC Number:

TP391.41

DONG Xiaohui GAO Ge CHEN Liang HAN Zhen JIANG Junjun. Noise face hallucination via data-driven local eigentransformation[J]. Journal of Computer Applications, 2014, 34(12): 3576-3579.

董小慧高戈陈亮韩镇江俊君. 数据驱动局部特征转换的噪声人脸幻构[J]. 计算机应用, 2014, 34(12): 3576-3579.

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Noise face hallucination via data-driven local eigentransformation

数据驱动局部特征转换的噪声人脸幻构

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