Face hallucination based on position prior and sparse representation

Journal of Computer Applications ›› 2012, Vol. 32 ›› Issue (05): 1300-1302.

• Network and distributed techno • Previous Articles Next Articles

Face hallucination based on position prior and sparse representation

MA Xiang¹,²

1. School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an Shaanxi 710049, China
2. School of Information Engineering, Chang'an University, Xi'an Shaanxi 710064, China

Received:2011-10-25 Revised:2011-12-08 Online:2012-05-01 Published:2012-05-01
Contact: MA Xiang

结合位置先验与稀疏表示的单帧人脸图像超分辨率算法

马祥¹,²

1. 长安大学信息工程学院，西安 710064
2. 西安交通大学电子与信息工程学院，西安 710049

通讯作者: 马祥
作者简介:马祥(1978-)，男，甘肃平凉人，讲师，博士，主要研究方向：图像分辨率增强与识别。
基金资助:
国家自然科学基金资助项目（61101215);中央高校基本科研业务费专项资金资助项目（CHD2011JC146）;长安大学基础研究支持计划专项基金资助项目

Abstract

Abstract: A face hallucination method based on sparse representation and position prior was proposed, which can obtain the enlargement of a single low-resolution input. Some perspectives of compressed sensing were applied to the method. The high- and low-resolution over-complete atoms were classified according to different positions of face. The low-resolution face image inputs were approximated by the sparse linear combination of the over-complete atoms which were classified. The sparse coefficients were obtained to reconstruct the high-resolution data of certain position. According to their original positions, the generated patches were integrated into a global face. The experimental results illustrate that the proposed method can generate satisfying high-resolution face image using fewer atoms compared to other methods.

Key words: sparse representation, compressed sensing, over-complete dictionary, position prior, face image, super-resolution

摘要： 提出了一种结合位置先验与稀疏表示的人脸图像超分辨率算法，可对单帧输入的低分辨率人脸图像基于训练集进行超分辨率重建。利用压缩感知理论中的信号分解方法，〖BP(〗明确哪些方法更好〖BP)〗，将稀疏表示与人脸位置先验信息相结合，使用经过分类的超完备冗余字典，来分别稀疏逼近输入信号的块向量结构。利用最佳的K项原子，线性组合重建出高分辨率图像块。最后按照图像块最初在人脸的位置，将它们拼接为整体人脸。在CAS-PEAL-R1人脸图库上的实验结果表明，该算法使用相对较少的原子，就可以重建出质量较好的高分辨率人脸图像。

关键词: 稀疏表示, 压缩感知, 超完备字典, 位置先验, 人脸图像, 超分辨率

CLC Number:

TP391.41

MA Xiang. Face hallucination based on position prior and sparse representation[J]. Journal of Computer Applications, 2012, 32(05): 1300-1302.

马祥. 结合位置先验与稀疏表示的单帧人脸图像超分辨率算法[J]. 计算机应用, 2012, 32(05): 1300-1302.

References

［1］DONOHO L D. Compressed sensing ［J］. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306.

［2］TEMLYAKOV V. Nonlinear methods of approximation ［R］. Los Angeles: University of South Carolina, Department of Mathematics, 2001.

［3］MALLAT S, ZHANG Z. Matching pursuits with time-frequency dictionaries ［J］. IEEE Transactions on Signal Processing, 1993, 41(12): 3397-3415.

［4］TROPP J A, GILBERT A C. Signal recovery from partial In-formation by orthogonal matching pursuit ［EB/OL］.(2005)[2011-10-01]. http://www.personal. umich. edu/ - jtropp/ papers/ TG052Signal2Re2 covery. pdf.

［5］CHEN S S, DONOHO D L, SAUNDERS M A. Atomicdecomposition by basis pursuit ［J］. SIAM Journal on Scientific Computing, 2001, 43(1): 129-159.

［6］DONOHO D L, ELAD M. Optimally sparse representation ingeneral (non-orthogonal) dictionaries via l1 minimization ［EB/OL］.［2011-09-08］. http://www-stat.stanford.edu/~donoho/Reports/2002/OptSparse.pdf.

［7］AHARON M, ELAD M, BRUCKSTEIN A. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation ［J］. IEEE Transactions on Signal Processing, 2006, 23(4): 4311-4322.

［8］GAO WEN, CAO BO, SHAN SHIQUAN, et al.The CAS-PEAL large-scale Chinese face database and baseline evaluations ［J］. IEEE Transactions on System Man, and Cybernetics: Part A, 2008, 38(1):149-161.

［9］MA X, ZHANG J P, QI C. Hallucinating face by position-patch ［J］. Pattern Recognition, 2010, 43(6): 2224-2236.

［10］WANG X G, TANG X O. Hallucinating face by eigen trans-formation ［J］. IEEE Transactions on Systems, Man and Cybernetics, 2005, 35(3): 425-434.

［11］CHANG H, YEUNG D Y, XIONG Y M. Super-resolution through neighbor embedding ［C］// Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE, 2004: 1275-1282.

［12］ZHUANG Y T, ZHANG J, WU F. Hallucinating faces: LPH super- resolution and neighbor reconstruction for residue compensation ［J］. Pattern Recognition, 2007, 40(9):3178-3194.

[1]	Lin GUO, Kunhu LIU, Chenyang MA, Youxue LAI, Yingfen XU. Image super-resolution reconstruction based on residual attention network with receptive field expansion [J]. Journal of Computer Applications, 2024, 44(5): 1579-1587.
[2]	Zihan LIU, Dengwen ZHOU, Yukai LIU. Image super-resolution network based on global dependency Transformer [J]. Journal of Computer Applications, 2024, 44(5): 1588-1596.
[3]	Yang LIU, Rong LIU, Ke FANG, Xinyue ZHANG, Guangxu WANG. Video super-resolution reconstruction network based on frame straddling optical flow [J]. Journal of Computer Applications, 2024, 44(4): 1277-1284.
[4]	Feng CAO, Xiaoling YANG, Jianbing YI, Jun LI. Contradiction separation super-deduction method and application [J]. Journal of Computer Applications, 2024, 44(10): 3074-3080.
[5]	Hao CHEN, Zhenping XIA, Cheng CHENG, Xing LIN-LI, Bowen ZHANG. Lightweight image super-resolution reconstruction network based on Transformer-CNN [J]. Journal of Computer Applications, 2024, 44(1): 292-299.
[6]	Min LIANG, Jiayi LIU, Jie LI. Image super-resolution reconstruction method based on iterative feedback and attention mechanism [J]. Journal of Computer Applications, 2023, 43(7): 2280-2287.
[7]	Lihua SHEN, Bo LI. Super-resolution reconstruction of lung CT images based on feature pyramid network and dense network [J]. Journal of Computer Applications, 2023, 43(5): 1612-1619.
[8]	Ying LI, Chao HUANG, Chengdong SUN, Yong XU. Single image super-resolution method based on residual shrinkage network in real complex scenes [J]. Journal of Computer Applications, 2023, 43(12): 3903-3910.
[9]	Xinhe ZHANG, Haoran TAN, Wenbo LYU. Low-complexity generalized space shift keying signal detection algorithm based on compressed sensing [J]. Journal of Computer Applications, 2023, 43(12): 3890-3895.
[10]	Yining WANG, Qingshan ZHAO, Pinle QIN, Yulan HU, Chunmei ZONG. Super-resolution reconstruction algorithm of medical image based on lightweight dense neural network [J]. Journal of Computer Applications, 2022, 42(8): 2586-2592.
[11]	Yingying ZHANG, Chao REN, Ce ZHU. Depth image super-resolution based on shape-adaptive non-local regression and non-local gradient regularization [J]. Journal of Computer Applications, 2022, 42(6): 1941-1949.
[12]	Jia LI, Yuanlin ZHENG, Kaiyang LIAO, Haojie LOU, Shiyu LI, Zehao CHEN. No-reference image quality assessment algorithm based on saliency deep features [J]. Journal of Computer Applications, 2022, 42(6): 1957-1964.
[13]	Yongchun BAO, Jianchen ZHANG, Shouxin DU, Junjun ZHANG. Multi-label classification algorithm based on non-negative matrix factorization and sparse representation [J]. Journal of Computer Applications, 2022, 42(5): 1375-1382.
[14]	Huifeng WANG, Yan XU, Yiming WEI, Huizhen WANG. Image super-resolution reconstruction based on parallel convolution and residual network [J]. Journal of Computer Applications, 2022, 42(5): 1570-1576.
[15]	Ye ZHANG, Rong LIU, Ming LIU, Ming CHEN. Image super-resolution reconstruction network based on multi-channel attention mechanism [J]. Journal of Computer Applications, 2022, 42(5): 1563-1569.

Face hallucination based on position prior and sparse representation

结合位置先验与稀疏表示的单帧人脸图像超分辨率算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics