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face hallucination with Weighted Nuclear Norm Constraint

Journal of Shanghai Jiaotong university(Science) 2018年第5期23卷 627-635页

作者： TANG Songze LI Heng XIAO Liang Department of Criminal Science and Technology Nanjing Forest Police CollegeNanjing 210023China Department of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China

face hallucination via patch-pairs leaning based methods has been wildly used in the past several years. Some position-patch based face hallucination methods have been proposed to improve the representation power of image patch and obtain the optimal regressive weighted vector. The rationale behind the position-patch based face hallucination is the fact that human face is always highly structured and consequently positioned and it plays an increasingly important role in the reconstruction. However, in the existing position-patch based methods,the probe image patch is usually represented as a linear combination of the corresponding patches of some training images, and the reconstruction residual is usually measured using the vector norm such as 1-norm and *** the vector norms neglect two-dimensional structures inside the residual, the final reconstruction performance is not very satisfactory. To cope with this problem, we present a weighted nuclear-norm constrained sparse coding(WNCSC) model for position-patch based face hallucination. In addition, an efficient algorithm for the WNCSC is developed using the alternating direction method of multipliers(ADMM) and the method of augmented Lagrange multipliers(ALM). The advantages of the proposed model are twofold: in order to fully make use of low-rank structure information of the reconstruction residual, the weighted nuclear norm is applied to measure the residual matrix, which is able to alleviate the bias between input patches and training data, and it is more robust than the Euclidean distance(2-norm); the more flexible selection method for rank components can determine the optimal combination weights and adaptively choose the relevant and nearest hallucinated neighbors. Finally, experimental results prove that the proposed method outperforms the related state-of-the-art methods in both quantitative and visual comparisons.

关键词： face hallucination weighted nuclear norm position-patch

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A face hallucination Algorithm via an LLE Coefficients Prior Model

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Chinese Journal of Electronics 2018年第6期27卷 1234-1240页

作者： LI Xiaoguang JIA Guangheng LI Jiafeng ZHUO Li Signal and Information Processing Laboratory Beijing University of Technology

In the most of exiting Local linear embedding(LLE)-based image super-resolution methods, a Low resolution(LR) image can be represented as a linear combination of LR training samples. In these methods, the combination coefficients of the LR image are directly used to estimate the High resolution(HR) image. However, experimental results show that the LR-LLE coefficients are different from the corresponding HR-LLE coefficients. To bridge the gap between LR and HR images, a novel LLEbased face hallucination algorithm is proposed. An LLE coefficients prior model is introduced to reduce the coefficient errors. In this prior model, the LLE coefficients of the interpolated LR face image are used to constraint the reconstructed coefficients. Experimental results show that the proposed method can provide improved performance over the compared methods.

关键词： face hallucination Local linear embedding(LLE) Coefficients prior model Manifold learning

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face hallucination via compressive sensing

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Journal of Measurement Science and Instrumentation 2016年第2期7卷 149-154页

作者：杨学峰程耀瑜中北大学信息与通信工程学院山西太原030051

face hallucination or super-resolution is an inverse problem which is underdetermined,and the compressive sensing(CS)theory provides an effective way of seeking inverse problem *** this paper,a novel compressive sensing based face hallucination method is presented,which is comprised of three steps:dictionary learning、sparse coding and solving maximum a posteriori(MAP)*** the first step,the K-SVD dictionary learning algorithm is adopted to obtain a dictionary which can sparsely represent high resolution(HR)face image *** the second step,we seek the sparsest representation for each low-resolution(LR)face image paches input using the learned dictionary,super resolution image blocks are obtained from the sparsest coefficients and dictionaries,which then are assembled into super-resolution(SR)***,MAP formulation is introduced to satisfy the consistency restrictive condition and obtain the higher quality HR *** experimental results demonstrate that our approach can achieve better super-resolution faces compared with other state-of-the-art method.

关键词： face image super-resolution image face hallucination compressive sensing(CS)

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Super-Resolution for face Image with an Improved K-NN Search Strategy

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China Communications 2016年第4期13卷 151-161页

作者： QU Shenming HU Ruimin CHEN Shihong JIANG Junjun WANG Zhongyuan ZHANG Maosheng National Engineering Research Center for Multimedia Software Computer School of Wuhan University China Collaborative Innovation Center of Geospatial Technology China School of Software Henan University China

Recently, neighbor embedding based face super-resolution(SR) methods have shown the ability for achieving high-quality face images, those methods are based on the assumption that the same neighborhoods are preserved in both low-resolution(LR) training set and high-resolution(HR) training set. However, due to the "one-to-many" mapping between the LR image and HR ones in practice, the neighborhood relationship of the LR patch in LR space is quite different with that of the HR counterpart, that is to say the neighborhood relationship obtained is not true. In this paper, we explore a novel and effective re-identified K-nearest neighbor(RIKNN) method to search neighbors of LR patch. Compared with other methods, our method uses the geometrical information of LR manifold and HR manifold simultaneously. In particular, it searches K-NN of LR patch in the LR space and refines the searching results by re-identifying in the HR space, thus giving rise to accurate K-NN and improved performance. A statistical analysis of the influence of the training set size and nearest neighbor number is given, experimental results on some public face databases show the superiority of our proposed scheme over state-of-the-art face hallucination approaches in terms of subjective and objective results as well as computational complexity.

关键词： face hallucination K-NN re-identify super-resolution manifold learning

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A Novel Method for Super-resolution

A Novel Method for Super-resolution

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2012 International Conference on Computer Science and Mathematics,Physical Education and Management（ICCMPE 2012）

作者： Xiao Ma Xiaojun Wang Yuhua Mao Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong China

In this paper, a novel method for face hallucination is proposed to maximize the visual quality of the recovered high-resolution face image. This method is suitable for video surveillance where the faces of interest are often of small scale. The originality is that both the low-frequency components and high-frequency characteristics of the face images are considered in the proposed method. Firstly, a fast edge- oriented interpolation method will be applied to strengthen the low-resolution input face. Next, the structural information is extracted by principle components analysis;the high- frequency detail of the interpolated face can be reconstructed by means of Eigentransformation, which is based on a training database. At last, a final high-resolution face image is output as the combination of both the low and high frequency face information. Experiments demonstrate that the output images under this new method have advantage of excellent visual identification.

关键词： face hallucination edge-oriented interpolation super-resolution eigentransformation

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