CN108109124A - Indefinite position picture watermark restorative procedure based on deep learning - Google Patents
Indefinite position picture watermark restorative procedure based on deep learning Download PDFInfo
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- CN108109124A CN108109124A CN201711443089.9A CN201711443089A CN108109124A CN 108109124 A CN108109124 A CN 108109124A CN 201711443089 A CN201711443089 A CN 201711443089A CN 108109124 A CN108109124 A CN 108109124A
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- 238000000034 method Methods 0.000 title claims abstract description 14
- 238000013135 deep learning Methods 0.000 title claims abstract description 11
- 238000013527 convolutional neural network Methods 0.000 claims description 9
- 238000003672 processing method Methods 0.000 claims description 9
- 230000004913 activation Effects 0.000 claims description 6
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 claims description 3
- 230000000750 progressive effect Effects 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- 238000005303 weighing Methods 0.000 claims description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims 2
- 238000013145 classification model Methods 0.000 claims 1
- 230000008901 benefit Effects 0.000 abstract description 3
- 238000011084 recovery Methods 0.000 abstract description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
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Classifications
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- G06T5/77—
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T1/00—General purpose image data processing
- G06T1/0021—Image watermarking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/40—Filling a planar surface by adding surface attributes, e.g. colour or texture
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
Abstract
The present invention relates to a kind of indefinite position picture watermark restorative procedures based on deep learning, which is characterized in that comprises the following steps：（1）The acquisition and pretreatment of training data；（2）Watermark parted pattern is trained；（3）Watermark region is filled.It is an advantage of the invention that：Machine substitutes manpower, cost-effective, liberates manpower；It operates more standardized, avoids artificial very different；Compared to simple watermark recovery technique, the present invention can be with Automatic-searching watermark location.
Description
Technical field
The present invention relates to the indefinite position picture watermark restorative procedures based on deep learning.
Background technology
The prior art is mostly to use picture fix tool artificial treatment picture or the spy by algorithm process specific position
The watermark of setting shape, can not accurately position the watermark location in picture, restore watermark automatically.
The work efficiency of existing way is extremely low, and cost is high, it is impossible to the processing of mass data is adapted to, it is difficult to face big data
The challenge in epoch.
The content of the invention
The defects of to overcome the prior art, the present invention provide a kind of indefinite position picture watermark reparation based on deep learning
Method, the technical scheme is that：
Indefinite position picture watermark restorative procedure based on deep learning, comprises the following steps：
（1）The acquisition and pretreatment of training data；
（2）Watermark parted pattern is trained；
（3）Watermark region is filled.
The step（1）Specific processing method be：
（1-1）The picture number Zhang Zuowei samples of watermark are selected, the watermark location size and shape of each pictures is different；With
Photoshop is marked the watermarking section of each pictures, and the template picture of mark is png forms, and no-watermark position is
Transparent color；
（1-2）To step（1-1）The template picture of middle mark carries out binary conversion treatment, and processing step is as follows：
（a）Prototype drawing is converted into gray-scale map；
（b）Gray-level histogram equalization processing is carried out to gray-scale map；
（c）Binaryzation is carried out with 16~32 threshold value；
（1-3）Model segmentation is carried out to the Prototype drawing of binaryzation, it is specific as follows：
（a）Template picture is identical with picture shape to be marked, travels through the position of template picture and can find and corresponding is waiting to mark
Remember the position on picture, select to travel through each pixel in template picture；
（b）If traversing the value of current pixel point as 0,29 *, 29 neighborhoods using centered on current pixel are cut as bearing
Sample if the value of current pixel point is 1, cuts 29*29 neighborhoods using centered on current pixel as positive sample；
（3）When positive sample quantity is much smaller than negative sample quantity, stochastical sampling is carried out to negative sample, makes positive and negative sample proportion equal
Weighing apparatus；
The step（2）Specific processing method be：Picture pixels point is in step（1）In be divided into and have watermark and no-watermark two
Kind, the neighborhood for having the pixel of watermark is positive sample, and the neighborhood of the pixel of no-watermark is negative sample, uses CNN convolutional neural networks
As disaggregated model, model is established based on CNN, model is divided into eight layers, first layer：Convolutional layer, convolution kernel 3*3, step-length 1, edge
Filling；The second layer：Convolutional layer, convolution kernel 3*3, step-length 1, edge filling；Third layer：Pond layer, pond core 2*2；4th layer：
Convolutional layer, convolution kernel 1*1, step-length 1；Layer 5：Convolutional layer, convolution kernel 1*1, step-length 1；Layer 6：Pond layer, Chi Huahe
2*2；Layer 7：Full context layer；8th layer：Full context layer；Activation primitive uses reLu,, activation primitive is above-mentioned the
First, two, four, five layers of use, eventually by cross entropy loss function counting loss into backpropagation；Pass through training, automatic identification
Watermark location, size and shape in picture.
The step（3）Specific processing method be：Pass through step（3）Middle watermark parted pattern obtains a watermark
Masterplate indicates watermark location, the picture of size and shape in picture；By the way of the progressive filling in edge, by watermark region
The circle of most edge one start, the pixel that likeness in form outer ring is filled to inner ring gradually, until filling entire watermark region.
It is an advantage of the invention that：Machine substitutes manpower, cost-effective, liberates manpower；Operate it is more standardized, avoid manually
It is very different；Compared to simple watermark recovery technique, the present invention can be with Automatic-searching watermark location.
Specific embodiment
The invention will now be further described with reference to specific embodiments, the advantages and features of the present invention will be with description and
It is apparent.But these embodiments are only exemplary, do not form any restrictions to the scope of the present invention.People in the art
Member it should be understood that without departing from the spirit and scope of the invention can to the details of technical solution of the present invention and form into
Row modifications or substitutions, but these modifications and replacement are each fallen in protection scope of the present invention.
Indefinite position picture watermark restorative procedure the present invention relates to a kind of 1, based on deep learning, comprises the following steps：
（1）The acquisition and pretreatment of training data；
（2）Watermark parted pattern is trained；
（3）Watermark region is filled.
The step（1）Specific processing method be：
（1-1）The picture number Zhang Zuowei samples of watermark are selected, the watermark location size and shape of each pictures is different；With
Photoshop is marked the watermarking section of each pictures, and the template picture of mark is png forms, and no-watermark position is
Transparent color；
（1-2）To step（1-1）The template picture of middle mark carries out binary conversion treatment, and processing step is as follows：
（a）Prototype drawing is converted into gray-scale map；
（b）Gray-level histogram equalization processing is carried out to gray-scale map；
（c）Binaryzation is carried out with 16~32 threshold value；
（1-3）Model segmentation is carried out to the Prototype drawing of binaryzation, it is specific as follows：
（a）Template picture is identical with picture shape to be marked, travels through the position of template picture and can find and corresponding is waiting to mark
Remember the position on picture, select to travel through each pixel in template picture；
（b）If traversing the value of current pixel point as 0,29 *, 29 neighborhoods using centered on current pixel are cut as bearing
Sample if the value of current pixel point is 1, cuts 29*29 neighborhoods using centered on current pixel as positive sample；
（3）When positive sample quantity is much smaller than negative sample quantity, stochastical sampling is carried out to negative sample, makes positive and negative sample proportion equal
Weighing apparatus；
The step（2）Specific processing method be：Picture pixels point is in step（1）In be divided into and have watermark and no-watermark two
Kind, the neighborhood for having the pixel of watermark is positive sample, and the neighborhood of the pixel of no-watermark is negative sample, uses CNN convolutional neural networks
As disaggregated model, model is established based on CNN, model is divided into eight layers, first layer：Convolutional layer, convolution kernel 3*3, step-length 1, edge
Filling；The second layer：Convolutional layer, convolution kernel 3*3, step-length 1, edge filling；Third layer：Pond layer, pond core 2*2；4th layer：
Convolutional layer, convolution kernel 1*1, step-length 1；Layer 5：Convolutional layer, convolution kernel 1*1, step-length 1；Layer 6：Pond layer, Chi Huahe
2*2；Layer 7：Full context layer；8th layer：Full context layer；Activation primitive uses reLu,, activation primitive is above-mentioned the
First, two, four, five layers of use, eventually by cross entropy loss function counting loss into backpropagation；Pass through training, automatic identification
Watermark location, size and shape in picture.
The step（3）Specific processing method be：Pass through step（3）Middle watermark parted pattern obtains a watermark
Masterplate indicates watermark location, the picture of size and shape in picture；By the way of the progressive filling in edge, by watermark region
The circle of most edge one start, the pixel that likeness in form outer ring is filled to inner ring gradually, until filling entire watermark region.
Claims (4)
1. the indefinite position picture watermark restorative procedure based on deep learning, which is characterized in that comprise the following steps：
（1）The acquisition and pretreatment of training data；
（2）Watermark parted pattern is trained；
（3）Watermark region is filled.
2. the indefinite position picture watermark restorative procedure according to claim 1 based on deep learning, which is characterized in that institute
The step of stating（1）Specific processing method be：
（1-1）The picture number Zhang Zuowei samples of watermark are selected, the watermark location size and shape of each pictures is different；With
Photoshop is marked the watermarking section of each pictures, and the template picture of mark is png forms, and no-watermark position is
Transparent color；
（1-2）To step（1-1）The template picture of middle mark carries out binary conversion treatment, and processing step is as follows：
（a）Prototype drawing is converted into gray-scale map；
（b）Gray-level histogram equalization processing is carried out to gray-scale map；
（c）Binaryzation is carried out with 16~32 threshold value,
（1-3）Model segmentation is carried out to the Prototype drawing of binaryzation, it is specific as follows：
（a）Template picture is identical with picture shape to be marked, travels through the position of template picture and can find and corresponding is waiting to mark
Remember the position on picture, select to travel through each pixel in template picture；
（b）If traversing the value of current pixel point as 0,29 *, 29 neighborhoods using centered on current pixel are cut as bearing
Sample if the value of current pixel point is 1, cuts 29*29 neighborhoods using centered on current pixel as positive sample；
（3）When positive sample quantity is much smaller than negative sample quantity, stochastical sampling is carried out to negative sample, makes positive and negative sample proportion equal
Weighing apparatus.
3. the indefinite position picture watermark restorative procedure according to claim 1 based on deep learning, which is characterized in that institute
The step of stating（2）Specific processing method be：Picture pixels point is in step（1）In be divided into and have two kinds of watermark and no-watermark, have water
The neighborhood of the pixel of print is positive sample, and the neighborhood of the pixel of no-watermark is negative sample, using CNN convolutional neural networks as classification
Model establishes model based on CNN, and model is divided into eight layers, first layer：Convolutional layer, convolution kernel 3*3, step-length 1, edge filling；The
Two layers：Convolutional layer, convolution kernel 3*3, step-length 1, edge filling；Third layer：Pond layer, pond core 2*2；4th layer：Convolutional layer,
Convolution kernel 1*1, step-length 1；Layer 5：Convolutional layer, convolution kernel 1*1, step-length 1；Layer 6：Pond layer, pond core 2*2；The
Seven layers：Full context layer；8th layer：Full context layer；Activation primitive uses reLu,, activation primitive is above-mentioned first and second, four,
Five layers of use, eventually by cross entropy loss function counting loss into backpropagation；By training, the water in automatic identification picture
Print position, size and shape.
4. the indefinite position picture watermark restorative procedure according to claim 1 based on deep learning, which is characterized in that institute
The step of stating（3）Specific processing method be：Pass through step（3）Middle watermark parted pattern obtains a watermark masterplate, that is, indicates
Watermark location, the picture of size and shape in picture；By the way of the progressive filling in edge, enclosed by the most edge one of watermark region
Start, the pixel that likeness in form outer ring is filled to inner ring gradually, until filling entire watermark region.
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| CN111199175A (en) * | 2018-11-20 | 2020-05-26 | 株式会社日立制作所 | Training method and device for target detection network model |
| CN111435544A (en) * | 2019-01-14 | 2020-07-21 | 珠海格力电器股份有限公司 | Picture processing method and device |
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Inventor after: Li Zuochao Inventor after: Bai Junfeng Inventor after: Zhang Wenzhan Inventor after: Liu Ziyao Inventor after: Su Weijie Inventor before: Bai Junfeng Inventor before: Zhang Wenzhan Inventor before: Liu Ziyao Inventor before: Su Weijie |
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