CN108269245A - A kind of eyes image restorative procedure based on novel generation confrontation network - Google Patents
A kind of eyes image restorative procedure based on novel generation confrontation network Download PDFInfo
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Abstract
The present invention proposes a kind of eyes image restorative procedure based on novel generation confrontation network, and main contents include：Eyes image reparation, novel generation confrontation network, model framework, its process fights network (ExGAN) to introduce novel generation, generator is instructed using second photographed image-related information created by generator, when repairing face-image, it is used as using same person in the second image of different time or different gestures with reference to image, ExGAN is while original photo feature is retained, using reference data, the method for proposing to propose to be repaired based on reference picture and code respectively in ExGAN.The present invention is repaired using the sample information in reference picture region, code, which is perceived, using one describes the object, the inserting extra information on multiple points in fighting network, increases its descriptive power, is conducive to generate high quality, lifelike individualized therapy effect as original image.
Description
Technical field
The present invention relates to image processing field, more particularly, to a kind of eyes image based on novel generation confrontation network
Restorative procedure.
Background technology
Eyes image reparation is an important content in image procossing, the purpose is to using the existing information of image come extensive
The information lost again is usually used in the fields such as public security, traffic, medicine, military investigation.Specifically, in police field, available for public security
The recovery of face detection and imperfect picture in picture.In field of traffic, image repair can be utilized to analyze traffic accident,
The noise of removal accident picture.In medical domain, X-ray lung images is contributed to increase clear, ultrasonography processing etc..And in military affairs
Field, the key message that eyes image is read in criminal evidence obtaining are most important.But in public security, medicine, military surveillance and day
Usually due to the distortion of picture pick-up device optical system, focus inaccurate, relative motion or personage's blink in the In vivo detection often lived
Eye closing etc. causes the fuzzy of image so that and the extraction of information becomes difficult, and the influence of noise eliminated in image is also more and more important,
For example identify that suspect, extraction proves or carry out technical appraisement from public security criminal image data from the crowd quickly moved
Etc., these important applications are required for removing distortion as much as possible by image repair technology, and it is extensive to carry out personage's eye closing image
It is multiple etc., therefore have important practical significance for the technical research of eyes image reparation.
The present invention proposes a kind of eyes image restorative procedure based on novel generation confrontation network, introduces novel generation pair
Anti- network (ExGAN) instructs generator using second photographed image-related information created by generator, with more and more numbers
It is developed according to collection, it is assumed that the second image of special object rationally exists, when repairing face-image, using same person when different
Between or the second image of different gestures be used as with reference to image, ExGAN utilizes reference number while original photo feature is retained
According to the method for proposing to be repaired based on reference picture and code.The present invention using the sample information in reference picture region into
Row is repaired, and perceiving code using one describes the object, and the inserting extra information on multiple points in fighting network increases it and retouches
Ability is stated, is conducive to generate high quality, lifelike individualized therapy effect as original image.
Invention content
For eyes image reparation, the present invention proposes a kind of eyes image reparation side based on novel generation confrontation network
Method, introduces novel generation confrontation network (ExGAN), and generation is instructed using second photographed image-related information created by generator
Device when repairing face-image, is used as in the second image of different time or different gestures with reference to image using same person,
ExGAN, using reference data, proposes to be repaired based on reference picture and code respectively while original photo feature is retained
Method.
To solve the above problems, propose a kind of eyes image restorative procedure based on novel generation confrontation network, master
Content is wanted to include：
(1) eyes image reparation；
(2) novel generation confrontation network；
(3) model framework.
Wherein, the eyes image reparation, is repaired using the sample information in reference picture region, uses one
It perceives code and describes the object, the inserting extra information on multiple points in fighting network increases its descriptive power, generates trueer
Real repairing effect.
Wherein, novel generation confrontation network, introduces novel generation confrontation network (ExGAN), and generator creates figure
As after, second image is obtained, generator is instructed using the relevant information of second image, with more and more data set quilts
Exploitation, it is assumed that the second image of special object rationally exists, when repairing face-image, using same person in different time or not
The second image with posture is used as with reference to image, these information are merged into a semantic guide, generation is just by network by study
True reparation is as a result, ExGAN while original photo feature is retained, using reference data, proposes two kinds of independences in ExGAN
Method repair image：It is the method repaired based on reference picture first, the reference picture r in generator GiAs to
It leads, reference picture r is used in discriminator DiCome whether determining generated image is true as additional information；Secondly it is based on
The restorative procedure of code creates in information area and perceives code ci, the compressed version of eye image is stored in vector
In, wherein each target is with additional information riAnd ciFor condition, additional content loss item is added for target.
Further, the eye image of the discriminator, the entire facial image of discriminator processing and amplification, overall situation confrontation
Property loss strengthen whole semantic consistency, local antagonism loss ensures generate the details and clarity of output, the overall situation
The output of convolution branch and local convolution branch forms a sigmoid function by connection, and reference picture is inputted in discriminator D,
Additional global convolution branch is added in discriminator, the output of three branches is connected.
Further, it is described to be repaired based on reference picture, training set xiIn each image it is right there are one
The reference picture r answeredi, training set X is defined as a tuple X={ (x1,r1), in eyes image reparation, riIt is xiIn it is same
The image of one people's difference posture, in xiMiddle removal Hotfix generation new images zi, learning objective is defined as：
In order to preferably generalize, in training set xiIn accordingly give one group of reference picture set Ri, training set is extended
To a tuple-set：X={ x1×R1,…,xn×Rn, the set by each needs reparation image and its reference picture it
Between cartesian product composition.
Further, the reparation based on code, the pixel number of each image is in data set | I |, it is assumed that there are
One compression functionWherein N ＜ ＜ | I |, for each image z for needing to repairiAnd its corresponding reference
Image ri, use riGenerate code ci=C (ri), in view of encoded sample information, is by confrontation object definition：
Wherein, compression function is a general depth network projected to example in some manifold, in equation (2) most
Latter is the generation image G (z in sensing regioni,ci) and original reference image riThe optional loss of distance, with measuring low-dimensional
It is corresponding that the distance between image and reference picture are generated in manifold, if generator G is complete convolution, as input ciWhen,
It needs to change its framework to handle any number of vector.
Further, the compression function perceives code c to generatei, one is trained individually for compression function C
Autocoder, during training C, encoder chooses single eyes as input, and the decoder of autocoder is divided into left and right
Branch corresponds to the different target of right and left eyes respectively, not repeated when ensuring the common trait of encoder study eyes, passes through volume
Code distinguishes feature, and each eye is all encoded with the floating point vector of 128 dimensions, is formed by combining these codings
The eye pattern coding of 256 dimensions.
Wherein, the model framework, has used a Standard convolution generator, and neck region includes the volume of expansion
Product, since generation eye image is more confined from than general repair content repair text, so the port number of network internal layer is less, in generator
One RGB image of middle input, the mobile part for needing to repair, stacks a single channel binary mask, indicates the area to be filled
Domain, generator show eyes by using the rgb value and another single channel mask of four additional channels and reference picture
Position, before training, all eye positions being detected store together with data set.
Further, the generator, the generator use the structure of encoder and decoder, share 4 down-samplings
With up-sampling layer, there is the full connection bottleneck layer of 256 dimensions, bottleneck layer is connect with eye code, and the overall dimensions of output are
512, eyes code loses item by the perception of equation (2), is attached in the fixed-size penultimate output of discriminator, by
Be more than two of original discriminator outputs, therefore before the last one sigmoid function in 256 dimensions of code, by one compared with
Small bilayer is fully connected network and global and local output code is tested, with this automatic study code and convolution discriminator
Between optimal weight.
Further, the data set, ExGAN need a data set for including each image pair, but due to this
The data set of type is not common, in order to evade the limitation of available data collection, develops about 2,000,000 2D alignment images and makees
For internal trainer collection, data set ensures everyone at least 3 images, and each image in training set includes an eye opening eyeball
Image, the high-definition picture shot under various environment and lighting condition enable ExGAN to it is various input photos into
Row internal repair, and noise-free picture and non-extreme posture image improve the eyes quality and clarity of generation.
Description of the drawings
Fig. 1 is a kind of system framework figure of the eyes image restorative procedure based on novel generation confrontation network of the present invention.
Fig. 2 is a kind of system flow chart of the eyes image restorative procedure based on novel generation confrontation network of the present invention.
Fig. 3 is that figure is compared in a kind of reconstruction loss of the eyes image restorative procedure based on novel generation confrontation network of the present invention
Fig. 4 is that a kind of sensing results of the eyes image restorative procedure based on novel generation confrontation network of the present invention compare
Figure.
Specific embodiment
It should be noted that in the absence of conflict, the feature in embodiment and embodiment in the application can phase
It mutually combines, the present invention is described in further detail in the following with reference to the drawings and specific embodiments.
Fig. 1 is a kind of system framework figure of the eyes image restorative procedure based on novel generation confrontation network of the present invention.It is main
To include eyes image reparation, novel generation confrontation network, model framework.
Wherein, the eyes image reparation, is repaired using the sample information in reference picture region, uses one
It perceives code and describes the object, the inserting extra information on multiple points in fighting network increases its descriptive power, generates trueer
Real repairing effect.
Wherein, novel generation confrontation network, introduces novel generation confrontation network (ExGAN), and generator creates figure
As after, second image is obtained, generator is instructed using the relevant information of second image, with more and more data set quilts
Exploitation, it is assumed that the second image of special object rationally exists, when repairing face-image, using same person in different time or not
The second image with posture is used as with reference to image, these information are merged into a semantic guide, generation is just by network by study
True reparation is as a result, ExGAN while original photo feature is retained, using reference data, proposes two kinds of independences in ExGAN
Method repair image：It is the method repaired based on reference picture first, the reference picture r in generator GiAs to
It leads, reference picture r is used in discriminator DiCome whether determining generated image is true as additional information；Secondly it is based on
The restorative procedure of code creates in information area and perceives code ci, the compressed version of eye image is stored in vector
In, wherein each target is with additional information riAnd ciFor condition, additional content loss item is added for target.
Further, the eye image of the discriminator, the entire facial image of discriminator processing and amplification, overall situation confrontation
Property loss strengthen whole semantic consistency, local antagonism loss ensures generate the details and clarity of output, the overall situation
The output of convolution branch and local convolution branch forms a sigmoid function by connection, and reference picture is inputted in discriminator D,
Additional global convolution branch is added in discriminator, the output of three branches is connected.
Further, it is described to be repaired based on reference picture, training set xiIn each image it is right there are one
The reference picture r answeredi, training set X is defined as a tuple X={ (x1,r1), in eyes image reparation, riIt is xiIn it is same
The image of one people's difference posture, in xiMiddle removal Hotfix generation new images zi, learning objective is defined as：
In order to preferably generalize, in training set xiIn accordingly give one group of reference picture set Ri, training set is extended
To a tuple-set：X={ x1×R1,…,xn×Rn, the set by each needs reparation image and its reference picture it
Between cartesian product composition.
Further, the reparation based on code, the pixel number of each image is in data set | I |, it is assumed that there are
One compression functionWherein N ＜ ＜ | I |, for each image z for needing to repairiAnd its corresponding ginseng
Examine image ri, use riGenerate code ci=C (ri), in view of encoded sample information, is by confrontation object definition：
Wherein, compression function is a general depth network projected to example in some manifold, in equation (2) most
Latter is the generation image G (z in sensing regioni,ci) and original reference image riThe optional loss of distance, with measuring low-dimensional
It is corresponding that the distance between image and reference picture are generated in manifold, if generator G is complete convolution, as input ciWhen,
It needs to change its framework to handle any number of vector.
Further, the compression function perceives code c to generatei, one is trained individually for compression function C
Autocoder, during training C, encoder chooses single eyes as input, and the decoder of autocoder is divided into left and right
Branch corresponds to the different target of right and left eyes respectively, not repeated when ensuring the common trait of encoder study eyes, passes through volume
Code distinguishes feature, and each eye is all encoded with the floating point vector of 128 dimensions, is formed by combining these codings
The eye pattern coding of 256 dimensions.
Wherein, the model framework, has used a Standard convolution generator, and neck region includes the volume of expansion
Product, since generation eye image is more confined from than general repair content repair text, so the port number of network internal layer is less, in generator
One RGB image of middle input, the mobile part for needing to repair, stacks a single channel binary mask, indicates the area to be filled
Domain, generator show eyes by using the rgb value and another single channel mask of four additional channels and reference picture
Position, before training, all eye positions being detected store together with data set.
Further, the generator, the generator use the structure of encoder and decoder, share 4 down-samplings
With up-sampling layer, there is the full connection bottleneck layer of 256 dimensions, bottleneck layer is connect with eye code, and the overall dimensions of output are
512, eyes code loses item by the perception of equation (2), is attached in the fixed-size penultimate output of discriminator, by
Be more than two of original discriminator outputs, therefore before the last one sigmoid function in 256 dimensions of code, by one compared with
Small bilayer is fully connected network and global and local output code is tested, with this automatic study code and convolution discriminator
Between optimal weight.
Further, the data set, ExGAN need a data set for including each image pair, but due to this
The data set of type is not common, in order to evade the limitation of available data collection, develops about 2,000,000 2D alignment images and makees
For internal trainer collection, data set ensures everyone at least 3 images, and each image in training set includes an eye opening eyeball
Image, the high-definition picture shot under various environment and lighting condition enable ExGAN to it is various input photos into
Row internal repair, and noise-free picture and non-extreme posture image improve the eyes quality and clarity of generation.
Fig. 2 is a kind of system flow chart of the eyes image restorative procedure based on novel generation confrontation network of the present invention.It is whole
Body training flow can be summarized as (1) and eyes marked from input picture；(2) using reference picture or code as guidance to image into
Row is repaired；(3) gradient to calculate generator parameter is lost by input picture and the reconstruct being repaired between image；(4) lead to
Reference picture is crossed, the image and uncalibrated image being repaired calculate the gradient of discriminator parameter；(5) it can be reversed by generator
The mistake of discriminator is propagated, updates the parameter of generator using loss is perceived, in the ExGAN based on reference picture, compression
Function is an identity function.
Fig. 3 is that a kind of reconstruction loss of the eyes image restorative procedure based on novel generation confrontation network of the present invention is compared
Figure.The figure shows the influences that ExGAN loses whole body reconstruction.With the increase of eye code, it can be clearly seen that based on generation
Code and the loss reduction amplitude based on reference picture are significantly greater than the content loss of non-GAN.
Fig. 4 is that a kind of sensing results of the eyes image restorative procedure based on novel generation confrontation network of the present invention compare
Figure.Compare the sensing results that generation confrontation network (GAN) and ExGAN are generated.(a) it is uncalibrated image, (b) is not use ExGAN
As a result, (c) is based on reference picture as a result, (d) is the result based on code.It can be seen from the figure that each ExGAN
Excellent sensing results are produced, wherein the result that the example model based on code generates is the most true to nature.
For those skilled in the art, the present invention is not limited to the details of above-described embodiment, in the essence without departing substantially from the present invention
In the case of refreshing and range, the present invention can be realized in other specific forms.In addition, those skilled in the art can be to this hair
Bright to carry out various modification and variations without departing from the spirit and scope of the present invention, these improvements and modifications also should be regarded as the present invention's
Protection domain.Therefore, appended claims are intended to be construed to include preferred embodiment and fall into all changes of the scope of the invention
More and change.
Claims (10)
1. a kind of eyes image restorative procedure based on novel generation confrontation network, which is characterized in that mainly including eyes image
It repairs (one)；Novel generation confrontation network (two)；Model framework (three).
2. based on the eyes image reparation (one) described in claims 1, which is characterized in that use the sample in reference picture region
This information is repaired, and perceiving code using one describes the object, the inserting extra information on multiple points in fighting network,
Increase its descriptive power, generate more true repairing effect.
3. the novel generation confrontation network (two) described in based on claims 1, which is characterized in that introduce novel generation confrontation net
Network (ExGAN) after generator creates image, obtains second image, generator is instructed using the relevant information of second image,
As more and more data sets are developed, it is assumed that the second image of special object rationally exists, and when repairing face-image, uses
Same person is used as in the second image of different time or different gestures with reference to image, and network is closed these information by study
And be a semantic guide, it generates and correctly repairs as a result, ExGAN utilizes reference number while original photo feature is retained
According to proposing that two kinds of independent methods repair image in ExGAN：It is the method repaired based on reference picture first,
Reference picture r in generator GiAs guide, reference picture r is used in discriminator DiIt determines to be generated as additional information
Image whether be true；Secondly the restorative procedure based on code, creates in information area and perceives code ci, by eye image
Compressed version is stored in vectorIn, wherein each target is with additional information riAnd ciFor condition, volume is added for target
Outer content loss item.
4. based on the discriminator described in claims 3, which is characterized in that the entire facial image of discriminator processing and the eye of amplification
Whole semantic consistency is strengthened in eyeball image, global antagonism loss, and local antagonism loss ensures to generate the details of output
And clarity, the output of global convolution branch and local convolution branch forms a sigmoid function by connection, in discriminator D
Additional global convolution branch is added in discriminator, the output of three branches is connected by middle input reference picture.
5. based on being repaired described in claim 3 based on reference picture, which is characterized in that training set xiIn each image
All there are a corresponding reference picture ri, training set X is defined as a tuple X={ (x1,r1), in eyes image reparation
In, riIt is xiThe image of middle same person difference posture, in xiMiddle removal Hotfix generation new images zi, learning objective is determined
Justice is：
In order to preferably generalize, in training set xiIn accordingly give one group of reference picture set Ri, training set is expanded to one
A tuple-set：X={ x1×R1,…,xn×Rn, which is repaired by each needs between image and its reference picture
Cartesian product forms.
6. based on the reparation based on code described in claim 3, which is characterized in that the pixel number of each image is in data set
| I |, it is assumed that there are a compression functionWherein N ＜ ＜ | I |, for each image z for needing to repairi
And its corresponding reference picture ri, use riGenerate code ci=C (ri), in view of encoded sample information, confrontation target is determined
Justice is：
Wherein, compression function is a general depth network projected to example in some manifold, last in equation (2)
Item is the generation image G (z in sensing regioni,ci) and original reference image riThe optional loss of distance, with measuring low dimensional manifold
Middle generation the distance between image and reference picture are corresponding, if generator G is complete convolution, as input ciWhen, it needs
Its framework is changed to handle any number of vector.
7. based on the compression function described in claims 6, which is characterized in that perceive code c to generatei, it is compression function C
One individual autocoder of training, during training C, encoder chooses single eyes and is used as input, autocoder
Decoder is divided into left and right branch, corresponds to the different target of right and left eyes respectively, with ensure encoder study eyes common trait when
It does not repeat, feature is distinguished by coding, each eye is all encoded with the floating point vector of 128 dimensions, passes through group
Close the eye pattern coding that these codings form 256 dimensions.
8. based on the model framework (five) described in claims 1, which is characterized in that a Standard convolution generator has been used,
Its neck region includes the convolution of expansion, since generation eye image is more confined from than general repair content repair text, so in network
The port number of layer is less, and a RGB image is inputted in generator, and the mobile part for needing to repair stacks a single channel two
System mask, indicates the region to be filled, generator by using four additional channels and reference picture rgb value and
Another single channel mask shows the position of eyes, and before training, all eye positions being detected are deposited together with data set
Storage.
9. based on the generator described in claims 8, which is characterized in that the generator uses the knot of encoder and decoder
Structure shares 4 down-samplings and up-sampling layer, has the full connection bottleneck layer of 256 dimensions, bottleneck layer is connect with eye code, defeated
The overall dimensions gone out are 512, and eyes code loses item by the perception of equation (2), is attached to the fixed-size inverse of discriminator
In second output, since 256 dimensions of code are more than two outputs of original discriminator, in the last one S-shaped letter
Before number, network is fully connected by a smaller bilayer, global and local output code is tested, with this automatic study
Optimal weight between code and convolution discriminator.
10. based on the data set described in claims 8, which is characterized in that ExGAN needs a number for including each image pair
According to collection, but since such data set is not common, in order to evade the limitation of available data collection, exploitation about 200
Ten thousand 2D are aligned images as internal trainer collection, and data set ensures everyone at least 3 images, each image in training set
All images comprising eye opening eyeball, the high-definition picture shot under various environment and lighting condition enable ExGAN
Internal repair is carried out, and noise-free picture and non-extreme posture image improve the eyes quality of generation to various input photos
And clarity.
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