CN103971332B - A kind of face image super-resolution restored method based on the constraint of HR-LLE weights - Google Patents
A kind of face image super-resolution restored method based on the constraint of HR-LLE weights Download PDFInfo
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Abstract
The invention belongs to digital image processing field, particularly to a kind of face image super-resolution restored method based on the constraint of HR LLE weights.First obtain a large amount of HR face sample and residual error HR face and rebuild weights constraint relative to the average of its neighbour's sample;In process of reconstruction, it is utilized respectively global and local and averagely rebuilds weights constraint and traditional human face super-resolution based on LLE is rebuild weights method for solving carry out weights constraint.Algorithm for reconstructing is divided into the overall situation to rebuild and local detail compensates two parts.Here the purpose that the overall situation is rebuild is the basic feature that backout criterion face should possess;The purpose that local detail compensates is to rebuild facial image to have the personal characteristics distinguishing other faces.The method, when estimating the reconstruction weights based on LLE of target HR image, adds the constraint of HR LLE weights, makes weights at l2Closer to real HR image reconstruction weights in norm.The method can obtain preferable image restoration result.
Description
Technical field
The invention belongs to digital image processing field, surpass particularly to a kind of facial image based on the constraint of HR-LLE weights
Resolution restored method.
Background technology
In recent years, the technology such as human face detection and recognition is in multimedia application such as video monitoring, mobile terminal and network retrievals
Middle play the most important effect.The performance of these multimedia application is had a very big impact by the quality of facial image.
But, owing to being affected by image capture device and collection environment, particularly under uncontrollable natural environment, get
Facial image the most second-rate, it is difficult to directly apply to follow-up detection and identification.Oversubscription is used after man face image acquiring
Resolution is restored (Super Resolution, SR) technology raising quality of human face image and is particularly important.
Existing human face super-resolution recovery technique can be divided into two classes: based on the method rebuild and side based on study
Method.In recent years, method based on study becomes the focus of research.Its main thought be by learning method set up low resolution and
Mapping relations between high-definition picture, machine learning the prior information obtained replaces based on artificially determining in method for reconstructing
The constraints of justice.Along with the development that manifold learning is theoretical, researcher proposes a series of facial image assumed based on manifold
Super-Resolution algorithm.Being locally linear embedding into (Locally Linear Embedding, LLE) is to have generation in manifold learning
A kind of Method of Nonlinear Dimensionality Reduction of table, is used for carrying out the Super-Resolution of image by Many researchers in recent years, achieves
Certain achievement.On the basis of human face super-resolution restoration algorithm based on LLE is all built upon manifold hypothesis, i.e. high-resolution
(High Resolution, HR) image (or image block) and corresponding low resolution (Low Resolution, LR) image (or
Image block) there is similar local geometry.Specifically in LLE, then show as in LR and HR space corresponding pixel or
When image block carries out linear expression by pixel about or block, weighted vector is equal.This hypothesis is applied to image oversubscription
During resolution is restored, initially set up the most paired LR-HR learning sample storehouse;Then for LR image to be reconstructed, sample is utilized
LR sample in storehouse carries out linear expression, it is thus achieved that LR weights coefficient.Process of reconstruction uses LR weights coefficient directly to replace HR weights
Coefficient, HR image is predicted in the HR linearity combination corresponding with Sample Storehouse.But, owing to the mapping in LR to HR space is one
To many mapping relations, there is non-equidistant in the most this mapping.Directly replace HR space weights with the weights in LR space will draw
Enter error.
Summary of the invention
It is an object of the invention to, by a kind of face image super-resolution recovery side based on the constraint of HR-LLE weights
Method, is redeveloped into the image that resolution is higher by low-resolution image.Here high-resolution refer to spatial resolution amplify 4 times or
More than 4 times.Present invention is generally directed to is facial image.
The present invention is to use techniques below means to realize:
First obtain a large amount of HR face sample and residual error HR face and rebuild weights constraint relative to the average of its neighbour's sample;
In process of reconstruction, it is utilized respectively global and local and averagely rebuilds weights constraint to traditional human face super-resolution based on LLE
Rebuild weights method for solving and carry out weights constraint.Overall flow figure is as shown in Figure 1.Algorithm for reconstructing is divided into the overall situation to rebuild and local
Details compensates two parts.Here the purpose that the overall situation is rebuild is the basic feature that backout criterion face should possess;Local detail is mended
The purpose repaid is to rebuild facial image to have the personal characteristics distinguishing other faces;
The method specifically includes following steps:
(1) overall situation HR-LLE weights constraint reestablishing
(1) overall situation is average rebuilds weights constraint
Initially set up paired HR face sample image storehouse and LR face sample image storehouse;Utilize Euclidean distance as measurement
Standard finds out the K of each LR face sample successively1Individual arest neighbors LR face sample and K corresponding thereto1Individual HR face sample graph
Picture;Traditional weights method for solving based on LLE is utilized to calculate LR face sample relative to its K1Individual LR nearest samples
Rebuild weights;Traditional weights method for solving based on LLE is utilized to show that HR face sample is relative to K1Individual HR nearest samples
Reconstruction weights, weight results is as shown in Figure 2;There is difference between LR weights and HR weights, this difference shows weights
The overall heaving tendency of coefficient is consistent, but variance is different;The reconstruction weights of LR sample and HR sample are sought l2Norm, result is such as
Shown in accompanying drawing 3.The l rebuilding weights of HR sample2Norm is to fluctuate in a scope the least, takes a large amount of HR sample weight
Build the l of weights2The meansigma methods of norm averagely rebuilds weights constraint W as the overall situationg。
(2) overall situation is rebuild
Input LR facial image, utilizes Euclidean distance to find out K in LR face Sample Storehouse1Individual LR nearest samples.Utilize
The reconstruction weights optimization method based on HR-LLE that the present invention proposes show that input LR facial image is relative to its K1Individual LR is
The reconstruction weights of neighbour's sample;Find out K1The HR sample that individual LR nearest samples is corresponding;Utilize and rebuild weights and K1Individual HR sample
Carry out linear combination and obtain the facial image of overall situation reconstruction.
(2) local detail compensates
(1) local average rebuilds weights constraint
First, HR residual error face Sample Storehouse and corresponding LR residual error face Sample Storehouse are set up;By the LR image profit in sample
One group of HR image initially amplified is generated with overall situation HR-LLE weights bounding algorithm;Calculate HR image in Sample Storehouse initial with these
Amplify the residual error between HR image, obtain one group of HR residual sample storehouse;Then the initial HR face sample image amplified is carried out
Down-sampling, calculates the residual error between subimage and the LR face sample image after down-sampling, obtains one group of LR residual error face sample
Image.
Owing to being that piecemeal is carried out when local detail compensates, so asking for the when that local average rebuilding weights constraint also
The method using piecemeal.The method for solving averagely rebuilding weights constraint to the overall situation is similar, the reconstruction power of HR residual sample image block
The l of value2Norm is also to fluctuate in a scope the least, therefore takes a large amount of HR residual sample block and rebuilds the l of weights2Model
The meansigma methods of number rebuilds weights constraint W as local averagel。
(2) local detail compensates
The facial image rebuilding the overall situation carries out down-sampling process;Down-sampled images is done with input LR facial image
Difference obtains LR residual error facial image;LR residual error facial image is carried out piecemeal process;Utilize Euclidean distance at LR residual error face sample
This storehouse is found out the K of each LR residual image block successively2Individual arest neighbors LR residual sample image block;Find out this K2Individual arest neighbors LR
The HR residual sample block that residual sample image block is corresponding;Utilize the reconstruction weights optimization based on HR-LLE that the present invention proposes
Method show that input LR residual error facial image block is relative to K2The reconstruction weights of individual LR arest neighbors residual sample block;Utilize this reconstruction
Weights and K2The linear combination of individual HR residual sample block obtains the HR residual image block rebuild.
Rebuild HR residual image block successively, finally reconstruct view picture HR residual error facial image;By this HR residual error facial image
It is added with the initial facial image that amplifies, obtains final output and amplify facial image.
It is pre-that useful the having the technical effect that of the present invention proposes a kind of HR image reconstruction weights based on the constraint of HR-LLE weights
Survey method.The method, when estimating the reconstruction weights based on LLE of target HR image, adds the constraint of HR-LLE weights, makes power
Value is at l2Closer to real HR image reconstruction weights in norm.On this basis, it is proposed that one is based on HR-LLE weights about
The face image super-resolution restored method of bundle.Relative to traditional images method for reconstructing, the method can obtain preferable image
Restoration result.
The feature of the present invention:
(1) a kind of target HR image reconstruction weights Forecasting Methodology based on the constraint of HR-LLE weights is proposed.The method exists
When estimating the reconstruction weights based on LLE of target HR image, add the constraint of HR-LLE weights, make weights at l2More connect in norm
Nearly true HR weights.
(2) a kind of face image super-resolution restored method based on the constraint of HR-LLE weights is proposed.The method is respectively
Retrain in terms of global characteristics, local feature two, rebuild high-resolution human face image.Experiment shows, context of methods is desirable
Obtain preferable high-resolution human face image reconstruction result.
Accompanying drawing illustrates:
Fig. 1, the inventive method entire block diagram
The reconstruction weights comparison diagram of Fig. 2, HR face image weights and 4 times of down-sampling LR facial images
Fig. 3, face image weights l2Norm and the weights l of 4 times of down-sampling LR faces2Norm contrasts
Fig. 4, input low-resolution face image
Fig. 5, tentatively amplify facial image
Fig. 6, final amplification export facial image
Fig. 7, the inventive method and tradition interpolation amplification results contrast
Detailed description of the invention:
Below in conjunction with Figure of description, the embodiment of the present invention is illustrated.
(1) overall situation HR-LLE weights constraint reestablishing
(1) overall situation is average rebuilds weights constraint
Firstly, it is necessary to set up two face image pattern storehouses, the most paired HR facial image Sample Storehouse and corresponding LR people
Face image pattern storehouse.This invention select CAS-PEAL facial image database and self-built China second-generation identity card image library have carried out reality
Test;Have selected 1470 width front face images altogether, be all normalized to 140 × 160 pixel sizes as the HR face in Sample Storehouse
Sample;4 times of down-sampling HR face samples, generate corresponding LR face sample.
Traditional weights method for solving based on LLE is utilized to calculate each LR face sample relative to its K1Arest neighbors sample
This reconstruction weights, as shown in Equation (1).
WhereinFor one of them LR face sample image,It is its K1Individual nearest samples and It is that it is relative to K1The reconstruction weights of individual nearest samples.In this invention, we are to K1Value
Test, find that, when it is more than 800, reconstruction effect is not obviously improved but time complexity but increases a lot.This
K is taken in bright1=800。
Traditional weights method for solving based on LLE is utilized to calculate the reconstruction weights of corresponding HR face sample, such as public affairs
Shown in formula (2).
WhereinBe withCorresponding HR face sample image,Be with
Corresponding K1Individual arest neighbors HR sample,It is that it is relative to K1The reconstruction weights of individual arest neighbors HR sample.
Solve optimization problem (1) and (2) and draw LR sample and the reconstruction weights of corresponding HR sample, example results such as accompanying drawing 2
Shown in.Wherein wLR represents the reconstruction weights of LR sample, and wHR represents the reconstruction weights of HR sample;It was found that LR weights and HR
There is difference between weights, this difference shows that the overall heaving tendency of weights coefficient is consistent, and variance is different.To LR sample
L is sought with the reconstruction weights of HR sample2Norm, example results is as shown in Figure 3.Wherein star dotted line represents that weights rebuild by LR sample
L2Norm, circular dashed line represents that the l of weights rebuild by HR sample2Norm;The l of weights rebuild by HR sample2Norm is the least at one
In the range of fluctuate, therefore take a large amount of HR sample rebuild weights l2The meansigma methods of norm averagely rebuilds weights about as the overall situation
Bundle Wg, this invention takes Wg=0.85。
(2) overall situation is rebuild
Input a width LR facial image x(to be not included in Sample Storehouse), size is 35 × 40 pixels, as shown in Figure 4;
Euclidean distance is utilized to find out the K of x from LR face Sample Storehouse1Individual arest neighbors face sampleUtilize
Formula (3) draw x relative toReconstruction weightsProfit again
The overall situation preliminary amplification facial image y is rebuild with formula (4)G, as shown in Figure 5.
WhereinIt isHR face sample corresponding in HR face Sample Storehouse.In (3), utilize the average weight of the overall situation
Build weights l2Norm constraint WgTo rebuilding weights WGRetraining, the reconstruction weights drawn after so solving optimization problem (3) are with regard to phase
The true weights of docking close-target HR facial image.Being a customized parameter, the reconstructed results obtained when taking different value is not
With, in this invention, we take=0.01.
(2) locally HR-LLE weights constraint details compensates
(1) local average rebuilds weights constraint
Initially set up HR residual error face sample and corresponding LR residual error face Sample Storehouse.Complete by the LR imagery exploitation in sample
Office's HR-LLE weights bounding algorithm generates one group and initially amplifies HR image pattern, calculates HR image in Sample Storehouse and initially puts with these
Residual error between big HR sample, obtains one group of HR residual sample storehouse, under then carrying out the initial HR face sample image amplified
Sampling, calculates the residual error between subimage and the LR face sample image after down-sampling, obtains LR residual error face sample image.
Owing to being that piecemeal is carried out when local detail compensates, so asking for the when that local average rebuilding weights constraint also
The method using piecemeal;HR residual error facial image is divided into the block of pixels of 8 × 8, corresponding LR residual error facial image by this invention
It is divided into the block of pixels of 2 × 2;The method for solving averagely rebuilding weights constraint to the overall situation is similar, the reconstruction of HR residual sample image block
The l of weights2Norm is also to fluctuate in a scope the least, takes a large amount of HR residual sample block and rebuilds weights l2Norm
Meansigma methods averagely rebuilds weights constraint W as the overall situationl, W in this inventionl=0.8。
(2) local detail compensates to generate and finally amplifies face
The overall situation is initially amplified face yGCarry out 4 times of down-samplings, then down-sampling face done difference with input LR face x,
Obtain inputting LR residual error face diffLR.LR residual error face diffLR is carried out piecemeal process (in order to ensure partial reconstruction image
Blocking effect between block, all has the overlap of a pixel between image block), block size is 2 × 2 pixels;From LR residual error face
Sample Storehouse sequentially finds each residual image blockK2Individual arest neighbors residual blockThis invention
Middle K2> 600 time, recovery effect is not improved, and therefore takes K2=600;Formula (5) is utilized to draw LR residual image blockRelatively
InReconstruction weights It is residual that recycling formula (6) rebuilds HR
Difference image block
WhereinIt isHR residual error facial image block corresponding in HR residual error face sample image block storehouse.In (5)
In, utilize local average to rebuild weights constraint WlTo rebuilding weights WLRetrain, draw after so solving optimization problem (5)
That rebuilds that weights are just in relatively close proximity to target HR residual error facial image block truly rebuilds weights.
Rebuild HR residual image block successively, finally reconstruct view picture HR residual image diffHR.By HR residual error facial image
DiffHR and initial amplification facial image yGIt is added, obtains final output and amplify facial image y, as shown in Figure 6.
Fig. 7 is the inventive method and tradition interpolation amplification methods and results comparison diagram.
Claims (1)
1. a face image super-resolution restored method based on the constraint of HR-LLE weights, it is characterised in that include walking as follows
Rapid:
(1) overall situation HR-LLE weights constraint reestablishing
(1) overall situation is average rebuilds weights constraint
Firstly, it is necessary to set up two face image pattern storehouses, the most paired HR facial image Sample Storehouse and corresponding LR face figure
As Sample Storehouse;
Weights method for solving based on LLE is utilized to calculate each LR face sample relative to its K1The reconstruction power of nearest samples
Value, as shown in formula (1);
WhereinFor one of them LR face sample image,It is its K1Individual nearest samples and It is that it is relative to K1The reconstruction weights of individual nearest samples;K1=800;
Weights method for solving based on LLE is utilized to calculate the reconstruction weights of corresponding HR face sample, as shown in formula (2);
WhereinBe withCorresponding HR face sample image,Be withRight
The K answered1Individual arest neighbors HR sample,It is that it is relative to K1The reconstruction weights of individual arest neighbors HR sample;
Solve optimization problem (1) and (2) and draw LR sample and the reconstruction weights of corresponding HR sample, take HR sample and rebuild weights l2Model
The meansigma methods of number averagely rebuilds weights constraint W as the overall situationg, Wg=0.85;
(2) overall situation is rebuild
Inputting a width LR facial image x, size is 35 × 40 pixels;Euclidean distance is utilized to find out x from LR face Sample Storehouse
K1Individual arest neighbors face sampleUtilize formula (3) draw x relative to
Reconstruction weightsRecycling formula (4) rebuilds the overall situation preliminary amplification facial image yG;
WhereinIt isHR face sample corresponding in HR face Sample Storehouse;In (3), the overall situation is utilized averagely to rebuild weights
l2Norm constraint WgTo rebuilding weights WGRetraining, the reconstruction weights drawn after so solving optimization problem (3) are just relatively close to
The true weights of target HR facial image;=0.01;
(2) locally HR-LLE weights constraint details compensates
(1) local average rebuilds weights constraint
Initially set up HR residual error face sample and corresponding LR residual error face Sample Storehouse;By the LR imagery exploitation overall situation in sample
HR-LLE weights bounding algorithm generates one group and initially amplifies HR image pattern, calculates HR image in Sample Storehouse and initially amplifies with these
Residual error between HR sample, obtains one group of HR residual sample storehouse, adopting under then carrying out the initial HR face sample image amplified
Sample, calculates the residual error between subimage and the LR face sample image after down-sampling, obtains LR residual error face sample image;
Owing to being that piecemeal is carried out when local detail compensates, so being also adopted by asking for local average reconstruction weights constraint when
The method of piecemeal;HR residual error facial image is divided into the block of pixels of 8 × 8, and corresponding LR residual error facial image is divided into the picture of 2 × 2
Element block;Take HR residual sample block and rebuild weights l2The meansigma methods of norm averagely rebuilds weights constraint W as the overall situationl, Wl=0.8;
(2) local detail compensates to generate and finally amplifies face
The overall situation is initially amplified face yGCarry out 4 times of down-samplings, then down-sampling face is done difference with input LR face x, obtain
Input LR residual error face diffLR;LR residual error face diffLR is carried out piecemeal process, and block size is 2 × 2 pixels;Residual from LR
Dispatch a person face Sample Storehouse sequentially finds each residual image blockK2Individual arest neighbors residual blockK2
=600;Formula (5) is utilized to draw LR residual image blockRelative toReconstruction weightsRecycling formula (6) rebuilds HR residual image block
WhereinIt isHR residual error facial image block corresponding in HR residual error face sample image block storehouse;In (5), utilize
Local average rebuilds weights constraint WlTo rebuilding weights WLRetrain, the reconstruction weights drawn after so solving optimization problem (5)
Just be in relatively close proximity to target HR residual error facial image block truly rebuilds weights;
Rebuild HR residual image block successively, finally reconstruct view picture HR residual image diffHR;By HR residual error facial image
DiffHR and initial amplification facial image yGIt is added, is finally exported amplification facial image y.
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