CN108734660A - A kind of image super-resolution rebuilding method and device based on deep learning - Google Patents
A kind of image super-resolution rebuilding method and device based on deep learning Download PDFInfo
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image
- G06T3/40—Scaling the whole image or part thereof
- G06T3/4053—Super resolution, i.e. output image resolution higher than sensor resolution
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/50—Image enhancement or restoration by the use of more than one image, e.g. averaging, subtraction
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
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Abstract
The invention discloses a kind of image super-resolution rebuilding method and device based on deep learning, described method includes following steps：Step S1 builds multiple dimensioned residual error convolutional neural networks, and carrying out multiple dimensioned residual error convolutional neural networks using training sample trains, to obtain the nonlinear correspondence relation of low high-definition picture block；Step S2 carries out Nonlinear Mapping using trained multiple dimensioned residual error convolutional neural networks to the low-resolution image of input, with the high-definition picture reconstructed, through the invention, the super-resolution image of reconstruction can be made closer to true image.
Description
Technical field
The present invention relates to Digital Image Processing, deep learning and artificial intelligence fields, more particularly to one kind based on deep
Spend the image super-resolution rebuilding method and device of study.
Background technology
The super-resolution rebuilding of image refers to being eliminated due to imaging system using the method for signal processing and computer software
Deteriroation of image quality caused by the factors such as inaccurate, motion blur and non-ideal sampling is focused, it is high-resolution clear to obtain
Clear image.It has important application valence in video monitoring, satellite image, medical image and some high-resolution display fields etc.
Value.
Since deep neural network has superpower nonlinear characteristic to indicate ability, the super resolution technology energy based on deep learning
Enough effects for improving image super-resolution well, at this stage, the super-resolution image reconstruction effect based on deep learning is
Pervious non-deep learning super-resolution technique is surmounted.But the image of resolution reconstruction at this stage and true image
There are still larger gaps, therefore, it is really necessary to propose a kind of technological means, so that the super-resolution image rebuild is closer
True image.
Invention content
In order to overcome the deficiencies of the above existing technologies, purpose of the present invention is to provide a kind of figures based on deep learning
As super resolution ratio reconstruction method and device, by combining residual error network and multiple dimensioned convolution advantage, using symmetrical more convolution kernels, make
The super-resolution image of reconstruction is closer to true image.
In view of the above and other objects, the present invention proposes a kind of image super-resolution rebuilding method based on deep learning,
Include the following steps：
Step S1 builds multiple dimensioned residual error convolutional neural networks, and multiple dimensioned residual error convolutional Neural is carried out using training sample
Network training, to obtain the nonlinear correspondence relation of low high-definition picture block；
Step S2 carries out the low-resolution image of input using trained multiple dimensioned residual error convolutional neural networks non-thread
Property mapping, with the high-definition picture reconstructed.
Preferably, step S1 further comprises：
Step S100 obtains training sample by the high-resolution and low-resolution image that several include identical content；
Step S101 builds multiple dimensioned residual error convolutional neural networks, and multiple dimensioned residual error is carried out using the training sample of acquisition
Convolutional neural networks learn, to establish the nonlinear correspondence relation between low-resolution image block and high-definition picture block.
Preferably, in step S100, the high-resolution and low-resolution image that several include identical content is obtained, to every
Width low-resolution image extracts low-resolution image block, extraction and low-resolution image block position in corresponding high-definition picture
Corresponding high-definition picture block is set, to obtain training sample.
Preferably, in step S100, the high-definition picture of high quality is first chosen, then to high-definition picture elder generation
Down-sampling up-samples again, obtains the low-resolution image with the high-definition picture same size.
Preferably, in step S101, the input of the multiple dimensioned residual error convolutional neural networks is and high-definition picture
An equal amount of low-resolution image block exports the high-definition picture block for reconstruct, the multiple dimensioned residual error convolutional Neural net
If network includes dried layer, the 1st layer and last layer are convolutional layer, if intermediate dried layer is multiple dimensioned residual error layer, the 1st layer of output with most
The output of later layer is added as the high-resolution result of reconstruct.
Preferably, if the centre dried layer of the multiple dimensioned residual error convolutional neural networks is with mutually isostructural multiple dimensioned residual
Poor unit, each multiple dimensioned residual unit include two multiple dimensioned convolution units, and centre is activation primitive, each multiple dimensioned convolution
Unit includes the convolutional network of 3 scales, and first scale is the convolutional network that core size is 1 × 1, and second scale is that core is big
The small convolutional network for being 3 × 3, third scale are 3 × 3 pondizations and 3 × 3 convolutional networks, and the channel figure of three scales passes through grade
Connection mode composition characteristic figure.
Preferably, the multiple dimensioned residual error convolutional neural networks are trained study using following loss function：
Wherein, N is training sample number, YiFor high-definition picture block,For the image block of network reconnection, | | | |1Table
Show L1 norms.
Preferably, in step S101, the damage is minimized using the backpropagation of stochastic gradient descent method and network
Function is lost, adjustment is optimized to the convolution nuclear parameter of multiple dimensioned residual error convolutional neural networks.
Preferably, step S2 further comprises：
Step S200 pre-processes the low-resolution image of input, to obtain the low resolution of target image size
Image；
The low-resolution image is divided into low-resolution image block to be reconstructed by step S201；
Step S202, using trained multiple dimensioned residual error convolutional neural networks to low-resolution image block respectively to be reconstructed
Carry out resolution reconstruction；
Step S203 carries out carry out fusion treatment to the high-definition picture block of all reconstruct of acquisition, is inputted
The reconstruct high-definition picture of image.
In order to achieve the above objectives, the present invention also provides a kind of image super-resolution rebuilding device based on deep learning, packet
It includes：
Training unit carries out multiple dimensioned residual error volume for building multiple dimensioned residual error convolutional neural networks using training sample
Product neural metwork training, to obtain the nonlinear correspondence relation of low high-definition picture block；
Reconstruction unit, for using trained multiple dimensioned residual error convolutional neural networks to the low-resolution image of input into
Row Nonlinear Mapping, with the high-definition picture reconstructed.
The prior art is compared, and the present invention a kind of image super-resolution rebuilding method and device based on deep learning pass through structure
It builds multiple dimensioned residual error convolutional neural networks, and combines residual error network and multiple dimensioned convolution advantage, by using symmetrical more convolution kernels,
The super-resolution reconstruction effect closer to true high-definition picture is achieved, it is closer with the super-resolution image for reaching reconstruction
The purpose of true image.
Description of the drawings
Fig. 1 is a kind of step flow chart of the image super-resolution rebuilding method based on deep learning of the present invention；
Fig. 2 is the detailed flowchart of step S1 in the specific embodiment of the invention；
Fig. 3 is the configuration diagram of multiple dimensioned residual error convolutional neural networks constructed in the specific embodiment of the invention；
Fig. 4 is the structural schematic diagram of multiple dimensioned residual unit in the specific embodiment of the invention；
Fig. 5 is the structural schematic diagram of multiple dimensioned convolution unit in the specific embodiment of the invention；
Fig. 6 is the detailed flowchart of step S2 in the specific embodiment of the invention；
Fig. 7 is a kind of system architecture diagram of the image super-resolution rebuilding device based on deep learning of the present invention；
Fig. 8 is the detail structure chart of training unit in the specific embodiment of the invention；
Fig. 9 is the detail structure chart of reconstruction unit in the specific embodiment of the invention.
Specific implementation mode
Below by way of specific specific example and embodiments of the present invention are described with reference to the drawings, those skilled in the art can
Understand the further advantage and effect of the present invention easily by content disclosed in the present specification.The present invention can also pass through other differences
Specific example implemented or applied, details in this specification can also be based on different perspectives and applications, without departing substantially from
Various modifications and change are carried out under the spirit of the present invention.
Depth convolutional neural networks structure is the core of image super-resolution, it has most critical to final quality reconstruction
Influence.The present invention is directed to key problem, it is proposed that utilizes multiple dimensioned residual error convolutional neural networks so that super-resolution reconstruction is imitated
Level of the fruit close to true high-definition picture.
Fig. 1 is a kind of step flow chart of the image super-resolution rebuilding method based on deep learning of the present invention.Such as Fig. 1 institutes
Show, a kind of image super-resolution rebuilding method based on deep learning of the present invention includes the following steps：
Step S1 builds multiple dimensioned residual error convolutional neural networks, and multiple dimensioned residual error convolutional Neural is carried out using training sample
Network training, to obtain the nonlinear correspondence relation of low high-definition picture block.In the specific embodiment of the invention, by more
Width includes that the high-resolution and low-resolution image of identical content obtains the multiple dimensioned residual error convolutional neural networks of training sample progress
It practises, to obtain the nonlinear correspondence relation of low high-definition picture block.
Specifically, as shown in Fig. 2, step S1 further comprises：
Step S100 obtains training sample by the high-resolution and low-resolution image that several include identical content.At this
In invention specific embodiment, the high-resolution and low-resolution image that several include identical content is obtained, to every width low resolution figure
As extracting low-resolution image block by the method for sliding window, extraction and low resolution in correspondence image high-definition picture
The corresponding high-definition picture block of tile location, to obtain training sample.
In fact, due to the high-definition picture that can not often obtain Same Scene simultaneously and corresponding low resolution figure
Picture, the present invention take analog form to obtain.Specifically, the high-definition picture of high quality is first chosen, for example 1000 width can be chosen
The high-definition picture that size is 1920 × 1080；Then full resolution pricture elder generation down-sampling is up-sampled again, is obtained and high-resolution
The low-resolution image of rate image same size.The method of upper down-sampling can be bilinearity, cubic spline, iterative backward projection
(IBP, iterative back-projection) etc..Finally use a certain size step-length (such as 60) by corresponding height
Image in different resolution is divided into the image block of 64 × 64 sizes respectively, as training image blocks sample
Step S101 builds multiple dimensioned residual error convolutional neural networks, and multiple dimensioned residual error is carried out using the training sample of acquisition
Convolutional neural networks learn, to establish the nonlinear correspondence relation between low-resolution image block and high-definition picture block.
Since residual error network is avoided that gradient disappears, so as to design the network of deeper number, preferably indicate non-thread
Property correspondence.In addition objects in images size is uncertain, and Analysis On Multi-scale Features can completely portray object.Symmetrical multireel product
Core increases the robustness of character representation, and therefore, the present invention establishes multiple dimensioned residual error convolutional neural networks, in conjunction with residual error network
With multiple dimensioned convolution advantage, symmetrical more convolution kernels are used.
Multiple dimensioned residual error convolutional neural networks constructed by the present invention are as shown in figure 3, its input is and high-definition picture
An equal amount of low-resolution image block exports the high-definition picture block for reconstruct, the multiple dimensioned residual error convolutional neural networks
One shares 34 layers, the 1st layer and the 34th layer as convolutional layer, and intermediate 32 layers are multiple dimensioned residual error layer.
1st layer and the 34th layer is all 3 × 3 convolutional layers.1st layer of output is added as the height of reconstruct with the 34th layer of output
Resolution ratio is as a result, their port number is all 3, corresponding tri- colors of RGB.
2nd layer to the 33rd layer is with mutually isostructural multiple dimensioned residual unit, internal structure is as shown in Figure 4.They
Channel number all be 256.Each multiple dimensioned residual unit contains two multiple dimensioned convolution units, and centre is activation primitive.
Activation primitive is common ReLU (Rectified Linear Units) function, is defined as follows：
F (x)=max (0, x),
Wherein, x is image convolution result.
Multiple dimensioned convolution unit is as shown in Figure 5.Multiple dimensioned convolution unit includes the convolutional network of 3 scales, first ruler
Degree is the convolutional network that core size is 1 × 1, and output channel figure number is 96, and second scale is the convolution net that core size is 3 × 3
Network, channel figure number are 96, and third scale is 3 × 3 ponds (taking 3x3 maximum values) and 3 × 3 convolutional networks, and channel figure number is 64,
The channel figure of three scales constitutes the characteristic pattern in 256 channels by cascade system.
Usually, four basic kinds can be symmetrically divided into：Symmetrical, horizontal symmetrical, vertical symmetry, diagonal line pair
Claim, then this four symmetrically by combination, 2 can be obtained4=16 kinds symmetrical, in this way, second ruler in multiple dimensioned convolution unit
It is original 1 that degree and third scale, which need 3 × 3 convolution kernel numbers preserved only,
16.When carrying out convolution operation, 3 × 3 convolution kernels of storage obtain 16 times of required storages by symmetry transformation
3 × 3 convolution kernels of number.
Specifically, the present invention is as follows by high-definition picture block and the image block counting loss function of reconstruction：
Wherein, N is training sample number, YiFor high-definition picture block,For the image block of network reconnection, | | | |1Table
Show L1 norms.Counting loss function is then based on the backpropagation of stochastic gradient descent method and network to adjust multiple dimensioned residual error
The convolution nuclear parameter of convolutional neural networks.This process is constantly repeated, counting loss functional value adjusts convolution nuclear parameter, until
Until loss function value is very small.
Then, it is minimized using the backpropagation (BP, Back Propagation) of stochastic gradient descent method and network
Loss function optimizes adjustment to the convolution nuclear parameter of multiple dimensioned residual error convolutional neural networks, constantly repeats this process, meter
Loss function value is calculated, convolution nuclear parameter is adjusted, until loss function value is very small.
Step S2 carries out the low-resolution image of input using trained multiple dimensioned residual error convolutional neural networks non-thread
Property mapping, to obtain the reconstruct high-definition picture of high quality.
Specifically, as shown in fig. 6, step S2 further comprises：
Step S200 pre-processes the low-resolution image of input, to obtain the low resolution of target image size
Image.In the specific embodiment of the invention, 3 channel RGB color images of the low resolution of input are up-sampled to obtain target figure
As the low-resolution image of size.Here top sampling method is identical as top sampling method when neural metwork training, for 2 times
Image super-resolution is it is necessary to which to 2 times of up-samplings of input low-resolution image, amplification method can be cubic spline interpolation, herein
It will not go into details.
Low-resolution image is divided into low-resolution image block to be reconstructed by step S201.Specifically, by low resolution
Image is divided into low-resolution image block to be reconstructed according to certain step-length, such as the size of image block can be 64 × 64,
Horizontal step-length and vertical step-length can all be 60, but invention is not limited thereto.
Step S202, using trained multiple dimensioned residual error convolutional neural networks to low-resolution image block respectively to be reconstructed
Carry out resolution reconstruction.Specifically, the low-resolution image block to be reconstructed divided is inputted into trained multiple dimensioned residual error
Convolutional neural networks, by network mapping, output is the high-definition picture block of reconstruct, that is, inputs the low resolution for 3 channels
Rate RGB color image block obtains high-resolution RGB image block by 34 layers of Processing with Neural Network.
Step S203 carries out carry out fusion treatment to the high-definition picture block of all reconstruct of acquisition, is inputted
The reconstruct high-definition picture of image.Specifically, the high-definition picture block of all reconstruct of acquisition is pressed into low resolution figure
It is averaged after being superimposed as block corresponding position, obtains the reconstruct high-definition picture of input picture.
Fig. 7 is a kind of system architecture diagram of the image super-resolution rebuilding device based on deep learning of the present invention.Step stream
Cheng Tu.As shown in fig. 7, a kind of image super-resolution rebuilding device based on deep learning of the present invention, including：
Training unit 70, for obtain several include identical content high-resolution and low-resolution image carry out it is multiple dimensioned
Residual error convolutional neural networks are trained, i.e., obtain training sample by the high-resolution and low-resolution image that several include identical content
The multiple dimensioned residual error convolutional neural networks study of this progress, to obtain the nonlinear correspondence relation of low high-definition picture block.
Specifically, as shown in figure 8, training unit 70 further comprises：
Training sample acquiring unit 701, for obtaining training sample.In the specific embodiment of the invention, training sample obtains
Take unit 701 by obtaining the high-resolution and low-resolution image that several include identical content, it is logical to every width low-resolution image
Cross the method extraction low-resolution image block of sliding window, extraction and low-resolution image in correspondence image high-definition picture
The corresponding high-definition picture block in block position, to obtain training sample.
In fact, due to the high-definition picture that can not often obtain Same Scene simultaneously and corresponding low resolution figure
Picture, the present invention take analog form to obtain.Specifically, training sample acquiring unit 701 first chooses the high resolution graphics of high quality
Picture, for example the high-definition picture that 1000 width sizes are 1920 × 1080 can be chosen；Then again to full resolution pricture elder generation down-sampling
Up-sampling, obtains the low-resolution image with high-definition picture same size.The method of upper down-sampling can be bilinearity, three
Secondary batten, iterative backward projection (IBP, iterative back-projection) etc..Finally use a certain size step-length
Corresponding high-low resolution image is divided into the image block of 64 × 64 sizes by (such as 60) respectively, as training image blocks sample
Network struction and training unit 702 utilize the instruction of acquisition for building multiple dimensioned residual error convolutional neural networks
Practice sample and carry out multiple dimensioned residual error convolutional neural networks study, to establish between low-resolution image block and high-definition picture block
Nonlinear correspondence relation.
It is convolutional layer that multiple dimensioned residual error convolutional neural networks one constructed by the present invention, which share 34 layers, the 1st layer and the 34th layer,
Intermediate 32 layers are multiple dimensioned residual error layer, input be with an equal amount of low-resolution image block of high-definition picture, export and be
The high-definition picture block of reconstruct.1st layer and the 34th layer is 3 × 3 convolutional layers.1st layer of output is added with the 34th layer of output
For reconstruct high-resolution as a result, their port number is all 3, tri- colors of corresponding RGB.
2nd layer to the 33rd layer is, with mutually isostructural multiple dimensioned residual unit, their channel number is all 256.Often
A multiple dimensioned residual unit contains two multiple dimensioned convolution units, and centre is activation primitive.Activation primitive is common ReLU
(Rectified Linear Units) function, is defined as follows：
F (x)=max (0, x).
Each multiple dimensioned convolution unit includes the convolutional network of 3 scales, and first scale is the convolution that core size is 1x1
Network, output channel figure number are 96, and second scale is the convolutional network that core size is 3x3, and channel figure number is 96, third ruler
Degree is 3 × 3 ponds (taking 3x3 maximum values) and 3x3 convolutional networks, and channel figure number is 64, and the channel figure of three scales passes through cascade
Mode constitutes the characteristic pattern in 256 channels.
Usually, four basic kinds can be symmetrically divided into：Symmetrical, horizontal symmetrical, vertical symmetry, diagonal line pair
Claim, then this four symmetrically by combination, 2 can be obtained4=16 kinds symmetrical, in this way, second ruler in multiple dimensioned convolution unit
It is original that degree and third scale, which need 3 × 3 convolution kernel numbers preserved only,When carrying out convolution operation, the 3 of storage
× 3 convolution kernels obtain 3 × 3 convolution kernels of 16 times of required storage numbers by symmetry transformation.
Specifically, for better resolution reconstruction effect, the present invention passes through high-definition picture block and the figure of reconstruction
As block counting loss function is as follows：
Wherein, N is training sample number, YiFor high-definition picture block,For the image block of network reconnection, | | | |1Table
Show L1 norms.
Then, network struction and training unit 702 using stochastic gradient descent method and network backpropagation (BP,
Back Propagation) loss function is minimized, the convolution nuclear parameter of multiple dimensioned residual error convolutional neural networks is carried out excellent
Change adjustment, constantly repeat this process, counting loss functional value adjusts convolution nuclear parameter, is until loss function value is very small
Only.
Reconstruction unit 71, for the low-resolution image using trained multiple dimensioned residual error convolutional neural networks to input
Nonlinear Mapping is carried out, to obtain the reconstruct high-definition picture of high quality.
Specifically, as shown in figure 9, reconstruction unit 71 further comprises：
Image pre-processing unit 710 is pre-processed for the low-resolution image to input, big to obtain target image
Small low-resolution image.In the specific embodiment of the invention, image pre-processing unit 710 leads to the 3 of the low resolution of input
Road RGB color image up-samples to obtain the low-resolution image of target image size.
Image segmentation unit 711, for pretreated low-resolution image to be divided into low resolution figure to be reconstructed
As block.Specifically, low-resolution image after pretreatment is divided by image segmentation unit 711 according to certain step-length waits for weight
The low-resolution image block of structure, such as the size of image block can be 64 × 64, horizontal step-length and vertical step-length can all be 60.
Resolution reconstruction unit 712, for using trained multiple dimensioned residual error convolutional neural networks to respectively to be reconstructed
Low-resolution image block carries out resolution reconstruction.Specifically, the low resolution to be reconstructed that resolution reconstruction unit 712 will have been divided
Rate image block inputs trained multiple dimensioned residual error convolutional neural networks, and by network mapping, output is the high-resolution of reconstruct
Rate image block, that is, input and obtain height by 34 layers of Processing with Neural Network for the low resolution RGB color image block in 3 channels
Differentiate RGB image block.
Fusion treatment unit 713, the high-definition picture block for all reconstruct to acquisition carry out fusion treatment, obtain
To the reconstruct high-definition picture of input picture.Specifically, fusion treatment unit 713 is by the high-resolution of all reconstruct of acquisition
Rate image block is averaged after being superimposed by low-resolution image block corresponding position, obtains the reconstruct high resolution graphics of input picture
Picture.
In conclusion the present invention a kind of image super-resolution rebuilding method and device based on deep learning are more by building
Scale residual error convolutional neural networks, and residual error network and multiple dimensioned convolution advantage are combined, by using symmetrical more convolution kernels, obtain
Closer to the super-resolution reconstruction effect of true high-definition picture, to reach the super-resolution image of reconstruction closer to true
Image purpose.
The above-described embodiments merely illustrate the principles and effects of the present invention, and is not intended to limit the present invention.Any
Field technology personnel can without violating the spirit and scope of the present invention, and modifications and changes are made to the above embodiments.Therefore,
The scope of the present invention, should be as listed in the claims.
Claims (10)
1. a kind of image super-resolution rebuilding method based on deep learning, includes the following steps：
Step S1 builds multiple dimensioned residual error convolutional neural networks, and multiple dimensioned residual error convolutional neural networks are carried out using training sample
Training, to obtain the nonlinear correspondence relation of low high-definition picture block；
Step S2 carries out non-linear reflect using trained multiple dimensioned residual error convolutional neural networks to the low-resolution image of input
It penetrates, with the high-definition picture reconstructed.
2. a kind of image super-resolution rebuilding method based on deep learning as described in claim 1, which is characterized in that step
S1 further comprises：
Step S100 obtains training sample by the high-resolution and low-resolution image that several include identical content；
Step S101 builds multiple dimensioned residual error convolutional neural networks, and multiple dimensioned residual error convolution is carried out using the training sample of acquisition
Neural network learning, to establish the nonlinear correspondence relation between low-resolution image block and high-definition picture block.
3. a kind of image super-resolution rebuilding method based on deep learning as claimed in claim 2, it is characterised in that：Yu Bu
In rapid S100, the high-resolution and low-resolution image that several include identical content is obtained, every width low-resolution image is extracted low
Image in different resolution block extracts high-definition picture corresponding with low-resolution image block position in corresponding high-definition picture
Block, to obtain training sample.
4. a kind of image super-resolution rebuilding method based on deep learning as claimed in claim 3, it is characterised in that：Yu Bu
In rapid S100, the high-definition picture of high quality is first chosen, then down-sampling up-samples again to the high-definition picture elder generation, obtains
With the low-resolution image of the high-definition picture same size.
5. a kind of image super-resolution rebuilding method based on deep learning as claimed in claim 2, it is characterised in that：Yu Bu
In rapid S101, the input of the multiple dimensioned residual error convolutional neural networks is and an equal amount of low resolution figure of high-definition picture
As block, export the high-definition picture block for reconstruct, if the multiple dimensioned residual error convolutional neural networks include dried layer, the 1st layer and
Last layer is convolutional layer, if intermediate dried layer is multiple dimensioned residual error layer, the 1st layer of output and the output of last layer are added as
The high-resolution result of reconstruct.
6. a kind of image super-resolution rebuilding method based on deep learning as claimed in claim 5, it is characterised in that：It is described
It is each multiple dimensioned residual if the centre dried layer of multiple dimensioned residual error convolutional neural networks is with mutually isostructural multiple dimensioned residual unit
Poor unit includes two multiple dimensioned convolution units, and centre is activation primitive, and each multiple dimensioned convolution unit includes the volume of 3 scales
Product network, first scale is the convolutional network that core size is 1 × 1, and second scale is the convolutional network that core size is 3 × 3,
Third scale is 3 × 3 pondizations and 3 × 3 convolutional networks, and the channel figure of three scales passes through cascade system composition characteristic figure.
7. a kind of image super-resolution rebuilding method based on deep learning as claimed in claim 6, which is characterized in that described
Multiple dimensioned residual error convolutional neural networks are trained study using following loss function：
Wherein, N is training sample number, YiFor high-definition picture block,For the image block of network reconnection, | | | |1Indicate L1
Norm.
8. a kind of image super-resolution rebuilding method based on deep learning as claimed in claim 7, it is characterised in that:Yu Bu
In rapid S101, the loss function is minimized using the backpropagation of stochastic gradient descent method and network, to multiple dimensioned residual error
The convolution nuclear parameter of convolutional neural networks optimizes adjustment.
9. a kind of image super-resolution rebuilding method based on deep learning as described in claim 1, it is characterised in that:Step
S2 further comprises：
Step S200 pre-processes the low-resolution image of input, to obtain the low-resolution image of target image size；
The low-resolution image is divided into low-resolution image block to be reconstructed by step S201；
Step S202 carries out low-resolution image block respectively to be reconstructed using trained multiple dimensioned residual error convolutional neural networks
Resolution reconstruction；
Step S203 carries out carry out fusion treatment to the high-definition picture block of all reconstruct of acquisition, obtains input picture
Reconstruct high-definition picture.
10. a kind of image super-resolution rebuilding device based on deep learning, including：
Training unit carries out multiple dimensioned residual error convolution god for building multiple dimensioned residual error convolutional neural networks using training sample
Through network training, to obtain the nonlinear correspondence relation of low high-definition picture block；
Reconstruction unit, it is non-for being carried out to the low-resolution image of input using trained multiple dimensioned residual error convolutional neural networks
Linear Mapping, with the high-definition picture reconstructed.
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