CN107833193A - A kind of simple lens global image restored method based on refinement network deep learning models - Google Patents
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Abstract
The present invention discloses a kind of simple lens global image restored method based on refinement network deep learning models.The data set of simple lens is obtained first, then the refinement network deep learning models restored for global image are built, the deep learning model can directly learn the characteristic of simple lens image space change, refinement network deep learning models are trained using obtained data set, for the blurred picture of the new shooting of simple lens, directly restored image can be quickly obtained using the model.The method proposed by the present invention simple lens image larger to fog-level spatial variability, without image is carried out into piecemeal processing, refinement network deep learnings model can directly learn the characteristic of fuzzy space change, make simple lens be calculated as picture processing procedure it is simpler quick, be easy to use in practice.
Description
Technical field
The present invention relates to a kind of image restoration field, refers specifically to one kind and is based on refinement-network deep learning moulds
The simple lens global image restored method of type.
Background technology
In recent years, unzoned lens is calculated as calculating one new research direction of photography as being increasingly becoming, with depth
The rise of learning algorithm is spent, the unzoned lens based on deep learning model occurs and calculates imaging method.Deep learning method can
To simulate the learning process of people's brain, the picture feature of correlation is extracted from lot of experimental data, and is trained, once model is instructed
White silk finishes, then using the characteristics of image of extraction, directly can carry out identical processing to new image using model.
The front end imaging device of unzoned lens only includes a piece of, two panels or three eyeglasses, by eyeglass aberration and the shadow of aberration
Ring, what the image directly shot by unzoned lens typically obscured, and the image that simple lens (only including a piece of eyeglass) obtains
With high blur spatial variability, i.e., the fog-level of fog-level and image border among image differs greatly.At present
It can not learn the fuzzy space variation characteristic of simple lens image, common place well for the deep learning model of unzoned lens
Reason method is that simple lens blurred picture is divided into several size identical image blocks first, by the fog-level of each image block
It is consistent to be approximately considered, and to each image block with after existing deep learning algorithm process, then different images block is stitched together,
Obtain final restored image.But the major defect of existing method is：Need individually to handle each image block, i.e.,
A corresponding deep learning model will be trained to each image block, spliced again after image restoration processing, consumes
Time-consuming length, operation is impetuous, and can influence image restoration precision during stitching image block to a certain extent.Therefore, design is fast
Fast effective deep learning simple lens image recovery method, it is that simple lens is calculated as urgent problem.
The content of the invention
The present invention is to overcome the above situation insufficient, it is desirable to provide one kind is based on refinement-network deep learning moulds
The simple lens global image restored method of type, the deep learning model designed by the present invention can effectively learn simple lens image
Fuzzy space variation characteristic, even if the simple lens image changed greatly to fuzzy space, it need to only train an overall global depth
Learning model is spent, realizes that the rapid image of simple lens end to end restores.
A kind of simple lens global image restored method based on refinement-network deep learning models, its feature
It is, comprises the following steps：
Step 1：Picture rich in detail corresponding to simple lens and blurred picture pair are generated, obtains data set；
Picture rich in detail corresponding to simple lens and blurred picture pair are generated in the step 1, obtains the specific method of data set
Comprise the following steps：
Step 1.1：Chessboard table images are displayed in full screen on computer screen, computer screen is shot with simple lens, is clapped
The chessboard table images taken the photograph；
Step 1.2：One picture rich in detail is displayed in full screen on computer screen, computer screen is shot with simple lens, obtains
The blurred picture of shooting；
Step 1.3：The chessboard table images shot based on step 1.1, using angular-point detection method, will be used in step 1.2
Picture rich in detail be mapped to corresponding position in the blurred picture of shooting, and by suitably cutting the simple lens precisely matched
Picture rich in detail and blurred picture pair；
Step 1.4：Prepare K different pictures rich in detail, be repeated in above-mentioned steps 1.2 and step 1.3, obtain K groups essence
The simple lens picture rich in detail and blurred picture pair of quasi- matching, that is, obtain data set.
Step 2：Build the refinement-network deep learning models restored for global image, the depth
The characteristic of simple lens image space change can directly be learnt by practising model；
It is used for the specific knot for the refinement-network deep learning models that global image restores in the step 2
Structure includes coding Encoder parts and decoding Decoder parts, wherein coding Encoder parts include six layers of convolutional layer c1,
C2, c3, c4, c5, c6, decoding Decoder parts include five sub- Internets, and each subnet network layers include four warp laminations
Deconv1, deconv2, deconv3 and deconv4, each subnet network layers of decoding Decoder parts are made to call during deconvolution
Encode convolutional layer characteristics of image corresponding to Encoder parts, the output result of last layer is used as next layer of input；This structure
Refinement-network deep learnings model can learn the fuzzy space variation characteristic of simple lens image.
Step 3：Deep learning model is trained using obtained data set, obtains the deep learning that training is completed
Model；；
Python or Caffe etc. can be selected often in the training of Encoder-Decoder deep learnings model in the step 3
See framework.
Step 4：For the blurred picture of the new shooting of simple lens, blurred picture is directly inputted to the depth trained and completed
Practise model, the picture rich in detail after quickly being restored.
Beneficial effect of the present invention：Refinement-network deep learnings model can effectively learn simple lens image
Fuzzy space variation characteristic, the simple lens image larger to fuzzy space intensity of variation, it need to only train an overall global depth
Learning model is spent, without splitting the image into small image block, deep learning is carried out to the small image block of different fog-levels respectively
Model training, the refinement-network deep learnings model designed by the present invention can make simple lens image restoration process
It is simpler quick, it is easy to use in practice.
Brief description of the drawings
Fig. 1 is the flow chart of the inventive method；
Fig. 2 is the chessboard table images of shooting；
Fig. 3 is the blurred picture shot in preprocessing process；
Fig. 4 is the picture rich in detail and blurred picture pair precisely matched；
Wherein (a) represents picture rich in detail, and (b) represents blurred picture.
Fig. 5 is the block schematic illustration of refinement-network deep learning models；
Fig. 6 is the structural representation of each sub-network in Decoder parts in refinement-network deep learning models
Figure；
Fig. 7 is the blurred picture that simple lens is newly shot；
Fig. 8 is the image restoration result based on refinement-network deep learning models；.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, rather than whole embodiments.Based on this
Embodiment in invention, the every other reality that those of ordinary skill in the art are obtained under the premise of creative work is not made
Example is applied, belongs to the scope of protection of the invention.
As shown in figure 1, a kind of list based on refinement-network deep learning models that the present embodiment provides is thoroughly
Mirror global image restored method, comprises the following steps：
Step 1：Picture rich in detail corresponding to simple lens and blurred picture pair are generated, obtains data set.Specific method is included such as
Lower step：
Step 1.1：Chessboard table images are displayed in full screen on computer screen, computer screen is shot with simple lens, is clapped
The chessboard table images taken the photograph, as shown in Figure 2；
Step 1.2：One picture rich in detail is displayed in full screen on computer screen, computer screen is shot with simple lens, obtains
The blurred picture of shooting, as shown in Figure 3；
Step 1.3：The chessboard table images shot based on step 1.1, using angular-point detection method, will be used in step 1.2
Picture rich in detail be mapped to corresponding position in the blurred picture of shooting, and by suitably cutting the simple lens precisely matched
Picture rich in detail and blurred picture pair, as shown in Figure 4；
Step 1.4：Prepare 10000 different pictures rich in detail, be repeated in above-mentioned steps 1.2 and step 1.3, obtain
10000 groups of simple lens pictures rich in detail precisely matched and blurred picture pair, i.e. refinement-network deep learnings model
Obtain data set.
Step 2：Build the refinement-network deep learning models restored for global image, the depth
The characteristic of simple lens image space change can directly be learnt by practising model.Wherein refinement-network deep learnings model
Concrete structure include coding Encoder parts and decoding Decoder parts, wherein coding Encoder parts include six layers of volume
Lamination c1, c2, c3, c4, c5, c6, decoding Decoder parts include five sub- Internets, and each subnet network layers include four
Warp lamination deconv1, deconv2, deconv3 and deconv4, each subnet network layers of decoding Decoder parts make warp
Call convolutional layer characteristics of image corresponding to coding Encoder parts during product, the output result of last layer is used as next layer of input；
The refinement-network deep learnings model of this structure can learn the fuzzy space variation characteristic of simple lens image.
Input picture size is 1080 × 1920, and every layer of parts of coding Encoder convolutional layer makees down-sampling, down-sampling first
Rate is 0.7, and every layer of parts of decoding Decoder warp lamination first up-samples, and up-sampling rate is similarly 0.7, the figure finally exported
As size is similarly 1080 × 1920, the size for making convolution kernel when convolution and deconvolution is 3 × 3.
Step 3：Deep learning model is trained using obtained data set, obtains the deep learning that training is completed
Model, using the Caffe environmental training refinement-network deep learning models in Ubuntu systems, use
ADAGRAD optimized algorithms are trained, and initial learning rate is 0.01, and frequency of training is 600000 times, wherein, it is in frequency of training
300000th, 400000 and 500000 when, learning rate respectively divided by 10, reduce learning rate, training complete refinement-
Network deep learnings model probably needs three day time.
Step 4：For the blurred picture of the new shooting of simple lens, as shown in fig. 6, directly blurred picture input has been trained
Into refinement-network deep learning models, the picture rich in detail after quickly being restored, restoration result such as Fig. 7 institutes
Show.
The above disclosed power for being only a kind of preferred embodiment of the present invention, the present invention can not being limited with this certainly
Sharp scope, therefore the equivalent variations made according to the claims in the present invention, still belong to the scope that the present invention is covered.
Claims (4)
1. a kind of simple lens global image restored method based on refinement-network deep learning models, its feature exist
In comprising the following steps：
Step 1：Picture rich in detail corresponding to simple lens and blurred picture pair are generated, obtains data set；
Step 2：Build the refinement-network deep learning models restored for global image, the deep learning mould
Type can directly learn the characteristic of simple lens image space change；
Step 3：Deep learning model is trained using obtained data set, obtains the deep learning model that training is completed；
Step 4：For the blurred picture of the new shooting of simple lens, blurred picture is directly inputted to the deep learning mould trained and completed
Type, the picture rich in detail after quickly being restored.
2. the simple lens global image according to claim 1 based on refinement-network deep learning models is answered
Former method, it is characterised in that：Picture rich in detail corresponding to simple lens and blurred picture pair are generated in the step 1, obtains data set
Specific method comprise the following steps：
Step 1.1：Chessboard table images are displayed in full screen on computer screen, computer screen is shot with simple lens, is shot
Chessboard table images；
Step 1.2：One picture rich in detail is displayed in full screen on computer screen, computer screen is shot with simple lens, is shot
Blurred picture；
Step 1.3：The chessboard table images shot based on step 1.1 are clear by what is used in step 1.2 using angular-point detection method
Clear image is mapped to corresponding position in the blurred picture of shooting, and by suitably cutting the clear of the simple lens precisely matched
Clear image and blurred picture pair；
Step 1.4：Prepare K different pictures rich in detail, be repeated in above-mentioned steps 1.2 and step 1.3, obtain accurate of K groups
The simple lens picture rich in detail matched somebody with somebody and blurred picture pair, that is, obtain data set.
3. the simple lens global image according to claim 1 based on refinement-network deep learning models is answered
Former method, it is characterised in that：It is used for the refinement-network deep learning moulds that global image restores in the step 2
The concrete structure of type includes coding Encoder parts and decoding Decoder parts, wherein coding Encoder parts include six layers
Convolutional layer c1, c2, c3, c4, c5, c6, decoding Decoder parts include five sub- Internets, and each subnet network layers include four
Individual warp lamination deconv1, deconv2, deconv3 and deconv4, each subnet network layers of decoding Decoder parts are made instead
Call convolutional layer characteristics of image corresponding to coding Encoder parts during convolution, the output result of last layer is defeated as next layer
Enter；The fuzzy space change that the refinement-network deep learnings model of this structure can learn simple lens image is special
Property.
4. the simple lens global image according to claim 1 based on refinement-network deep learning models is answered
Former method, it is characterised in that：The common frames such as Python or Caffe can be selected in the training of deep learning model in the step 4
Frame.
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Application publication date: 20180323 |