CN111353938A - Image super-resolution learning method based on network feedback - Google Patents

Image super-resolution learning method based on network feedback Download PDF

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CN111353938A
CN111353938A CN202010132826.9A CN202010132826A CN111353938A CN 111353938 A CN111353938 A CN 111353938A CN 202010132826 A CN202010132826 A CN 202010132826A CN 111353938 A CN111353938 A CN 111353938A
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颜成钢
楼杰栋
孙垚棋
张继勇
张勇东
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Hangzhou Dianzi University
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Abstract

The invention provides an image super-resolution learning method based on network feedback. The method comprises the steps of firstly processing a low-resolution image by using a convolution network to obtain a shallow feature, then connecting the shallow feature of the obtained low-resolution image and a high-level feature output by a feedback network at a previous moment through feedback to be used as the input of the feedback network, and correcting the high-level feature as one of low-level features through the feedback network to obtain a higher-level feature. And finally, deconvoluting the output of the feedback network, then performing convolution to obtain a residual image, and adding the image obtained by up-sampling the original image through bilinear interpolation to the residual image to obtain a super-resolution image. The invention solves the problems that the existing deep learning method does not utilize a feedback mechanism commonly existing in a human visual system, a plurality of high-resolution images correspond to the same low-resolution image, and the details of the image cannot be repaired due to the increase of a super-resolution scaling factor.

Description

Image super-resolution learning method based on network feedback
Technical Field
The invention relates to the technical field of image and video processing, in particular to an image super-resolution learning method based on network feedback.
Background
The super resolution reconstruction (SR) is an important image processing technology in the fields of computer vision and image processing, and because the image super resolution method can correct the damage of the image caused by equipment or environment to a certain extent, the method has important application values in many fields, such as target detection, medical imaging, security monitoring, satellite remote sensing, and the like.
In recent years, with the rapid development of deep learning, the deep learning is applied to various artificial intelligence tasks such as image classification and target detection, and has made breakthrough progress, researchers have also actively explored the use of deep learning to solve the super-resolution problem, and a more effective method, namely, a super-resolution method based on deep learning, is widely used to solve the super-resolution problem of images. By training an end-to-end network model, the mapping relation between the low-resolution image and the high-resolution image is directly learned.
With the various super-resolution methods based on deep learning, the method from the early convolutional neural network to the later antagonistic network generation shows good performance. However, the feedback mechanism ubiquitous in the human visual system has not been fully exploited in existing deep learning. The super-resolution problem is very challenging and not applicable since there are always multiple high-resolution images corresponding to the same low-resolution image, and furthermore, as the super-resolution scaling factor increases, the recovery of the missing details of the image becomes more complicated. Therefore, the information feedback is realized by using a hidden state in an RNN in a network feedback-based mode, high-level information is obtained through the feedback, and the final high-resolution image is generated iteratively.
Disclosure of Invention
Aiming at the problems that the existing deep learning method does not utilize a feedback mechanism commonly existing in a human visual system, a plurality of high-resolution images correspond to the same low-resolution image, and the details of the image cannot be repaired due to the increase of a super-resolution scaling factor, the invention provides an image super-resolution learning method based on network feedback.
An image super-resolution learning method based on network feedback comprises the following steps:
and (1) processing the low-resolution image by using a convolution network to obtain shallow features.
Image of low resolution
Figure BDA0002396260900000021
Inputting the data into a convolution network with 4 × m convolution kernels with the size of 3 × 3 and m convolution kernels with the size of 1 × 1 to obtain shallow layer characteristic output
Figure BDA0002396260900000022
As input to the feedback network. Low resolution image at next moment
Figure BDA0002396260900000023
By
Figure BDA0002396260900000024
Is obtained by down-sampling due to
Figure BDA0002396260900000025
And (3) for the super-resolution image of the initial reconstruction, taking the image obtained by sampling the reconstructed image as the input of the iteration at the next moment, and forming feedback to improve the later reconstruction effect.
And (2) taking the shallow feature of the obtained low-resolution image and the high-level feature output by the feedback network at the previous moment as the input of the feedback network through a feedback connection, and correcting the high-level feature as one of the low-level features through the feedback network to obtain a higher-level feature. The method comprises the following specific steps:
(2.1) shallow characterization
Figure BDA0002396260900000026
And advanced features
Figure BDA0002396260900000027
As a feedback network input, m convolutional layers with convolutional kernel size of 1 × 1 are used
Figure BDA0002396260900000028
And
Figure BDA0002396260900000029
concatenating and compressing, feeding back information
Figure BDA00023962609000000210
Refining input features produces refined input features
Figure BDA00023962609000000211
Wherein
Figure BDA00023962609000000212
Is composed of
Figure BDA00023962609000000213
Where the size of the convolution kernel k depends on the scaling factor.
(2.2) input features
Figure BDA00023962609000000214
Generating 1 high resolution feature by upsampling 1 deconvolution layer with m convolution kernels of size k × k and number m of convolution kernels
Figure BDA00023962609000000215
Characterizing high resolution
Figure BDA00023962609000000216
Convolution with 1 convolution kernel size of k × kThe convolution layer with m cores is subjected to up-sampling to obtain refined high-grade characteristics
Figure BDA00023962609000000217
(2.3) the refined high-level features are obtained
Figure BDA0002396260900000031
As input features, 1 convolution layer with convolution kernel size of 1 × 1 and convolution kernel number of m is added before convolution and deconvolution in step (2.2), the same operation as in step (2.2) is carried out, G times of repetition is carried out to obtain high-level feature vector group generated by low-level features
Figure BDA0002396260900000032
And corresponding refined feature vector groups
Figure BDA0002396260900000033
(2.4) in order that the feedback input at the next moment contains better characteristics, fusing the refined characteristics through 1-layer convolution layers with the convolution kernel size of 1 × 1 and the convolution kernel number of m to generate the output of the feedback network
Figure BDA0002396260900000034
And (3) deconvoluting the output of the feedback network, then obtaining a residual image by convolution, and adding the image obtained by up-sampling the original image through bilinear interpolation to the residual image to obtain a super-resolution image. The method comprises the following specific steps:
(3.1) feeding back the output of the network
Figure BDA0002396260900000035
And generating a high-resolution feature map by using the deconvolution layer with the convolution kernel size of k in m layers. Drawing the original
Figure BDA0002396260900000036
Obtaining a high resolution map by bilinear interpolation
Figure BDA0002396260900000037
(3.2) obtaining a residual image by passing the high-resolution feature map through a convolution layer with convolution kernel size of 3 × 3 of n layers
Figure BDA0002396260900000038
Finally, the large resolution map is processed
Figure BDA0002396260900000039
And residual image
Figure BDA00023962609000000310
Adding to obtain super-resolution image
Figure BDA00023962609000000311
When the image is a gray scale image, n is 1, and when the image is a color image, n is 3.
The invention has the following beneficial effects:
the invention solves the problems that the existing deep learning method does not utilize a feedback mechanism commonly existing in a human visual system, a plurality of high-resolution images correspond to the same low-resolution image, and the details of the image cannot be repaired due to the increase of a super-resolution scaling factor.
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FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a detailed view of a model of the method of the present invention;
FIG. 3 is a block diagram of the feedback network of the present invention;
fig. 4 is a supplementary explanatory diagram of the distribution-instruction feedback mechanism.
Detailed Description
The objects and effects of the present invention will become more apparent from the following detailed description of the present invention with reference to the accompanying drawings.
Fig. 1 is a flowchart of a method for learning super-resolution images based on network feedback according to the present invention, fig. 2 is a structural diagram of a method model, fig. 3 is a structural diagram of a feedback network, and fig. 4 is a supplementary explanatory diagram of a feedback mechanism.
The specific steps are as follows as shown in figure 1:
and (1) processing the low-resolution image by using a convolution network to obtain shallow layer characteristics.
Step (1.1) Low resolution image extraction according to the shallow feature extraction Module operation of FIG. 1
Figure BDA0002396260900000041
Inputting the data into a convolution network with 4 × m convolution kernels with the size of 3 × 3 and m convolution kernels with the size of 1 × 1 to obtain shallow layer characteristic output
Figure BDA0002396260900000042
As input to the feedback network.
Step (1.2) Low resolution image at the next moment
Figure BDA0002396260900000043
Super-resolution image for initial reconstruction
Figure BDA0002396260900000044
And the down sampling is obtained, and the reconstruction result is taken as a feedback to be introduced into the input, so that the subsequent reconstruction effect is improved. Specifically, the operation (a) and the down-sampling operation in the left part of the model fig. 2 are described.
And (2) inputting the shallow feature of the obtained low-resolution image and the high-level feature output by the feedback network at the previous moment into the feedback network to generate the refined high-level feature.
Step (2.1) shallow feature
Figure BDA0002396260900000045
And advanced features
Figure BDA0002396260900000046
As a feedback network input, m convolutional layers with convolutional kernel size of 1 × 1 are used
Figure BDA0002396260900000047
And
Figure BDA0002396260900000048
concatenating and compressing, feeding back information
Figure BDA0002396260900000049
Refining input features produces refined input features
Figure BDA00023962609000000410
Wherein
Figure BDA00023962609000000411
Is composed of
Figure BDA00023962609000000412
As shown in part a of fig. 3.
Step (2.2) input characteristics after thinning
Figure BDA00023962609000000413
1 high-resolution feature is generated by up-sampling 1 deconvolution layer with convolution kernel size k × k and convolution kernel number m
Figure BDA0002396260900000051
By
Figure BDA0002396260900000052
Upsampling 1 convolution layer with convolution kernel size of k × k and convolution kernel number of m to obtain refined high-level features
Figure BDA0002396260900000053
As shown in part b of fig. 3.
Step (2.3) is to obtain the refined advanced features
Figure BDA0002396260900000054
Adding 1 convolution layer with convolution kernel size of 1 × 1 and convolution kernel number of m before convolution and deconvolution in step (2.2) as input features, performing the same operation in step (2.2) for G times to obtain high-level feature vector group
Figure BDA0002396260900000055
And corresponding refined high-level feature vector group
Figure BDA0002396260900000056
As shown in part c of fig. 3
And (2.4) finally fusing the refined characteristics through 1 layer of convolutional layers with the convolutional kernel size of 1 × 1 and the convolutional kernels with the number of m to generate the output of the feedback network
Figure BDA0002396260900000057
As shown in part d of fig. 3
And (3) deconvoluting the output of the feedback network, then obtaining a residual image by convolution, and adding the image obtained by up-sampling the original image through bilinear interpolation to the residual image to obtain a super-resolution image.
Step (3.1) outputting the output obtained by the feedback module output module
Figure BDA0002396260900000058
And generating a high-resolution feature map by using the deconvolution layer with the convolution kernel size of k in m layers. Drawing the original
Figure BDA0002396260900000059
Obtaining a high resolution map by bilinear interpolation
Figure BDA00023962609000000510
Step (3.2) of obtaining a residual image by passing the high-resolution feature map through a convolution layer with the convolution kernel size of 3 × 3 (n layers)
Figure BDA00023962609000000511
Finally, the resolution map is large
Figure BDA00023962609000000512
And residual image
Figure BDA00023962609000000513
Adding to obtain super-resolution image
Figure BDA00023962609000000514
When the image is a gray scale image, n is 1, and when the image is a color image, n is 3.

Claims (1)

1. An image super-resolution learning method based on network feedback is characterized by comprising the following steps:
processing a low-resolution image by using a convolution network to obtain shallow layer characteristics;
image of low resolution
Figure FDA0002396260890000011
Inputting the data into a convolution network with 4 × m convolution kernels with the size of 3 × 3 and m convolution kernels with the size of 1 × 1 to obtain shallow layer characteristic output
Figure FDA0002396260890000012
As an input to a feedback network; low resolution image at next moment
Figure FDA0002396260890000013
By
Figure FDA0002396260890000014
Is obtained by down-sampling due to
Figure FDA0002396260890000015
For the super-resolution image reconstructed at the initial stage, taking the image obtained by sampling the reconstructed image as the input of iteration at the next moment to form feedback and improve the later reconstruction effect;
step (2), the shallow feature of the obtained low-resolution image and the high-level feature output by the feedback network at the previous moment are used as the input of the feedback network through feedback connection, and the high-level feature is used as one kind of low-level feature to be corrected through the feedback network to obtain a higher-level feature; the method comprises the following specific steps:
(2.1) shallow characterization
Figure FDA0002396260890000016
And advanced features
Figure FDA0002396260890000017
As a feedback network input, m convolutional layers with convolutional kernel size of 1 × 1 are used
Figure FDA0002396260890000018
And
Figure FDA0002396260890000019
concatenating and compressing, feeding back information
Figure FDA00023962608900000110
Refining input features produces refined input features
Figure FDA00023962608900000111
Wherein
Figure FDA00023962608900000112
Is composed of
Figure FDA00023962608900000113
Wherein the size of the convolution kernel k depends on the scaling factor;
(2.2) input features
Figure FDA00023962608900000114
Generating 1 high resolution feature by upsampling 1 deconvolution layer with m convolution kernels of size k × k and number m of convolution kernels
Figure FDA00023962608900000115
Characterizing high resolution
Figure FDA00023962608900000116
Upsampling by 1 convolutional layer with convolutional kernel size of k × k and convolutional kernel number of mTo obtain refined high-grade features
Figure FDA00023962608900000117
(2.3) the refined high-level features are obtained
Figure FDA00023962608900000118
As input features, 1 convolution layer with convolution kernel size of 1 × 1 and convolution kernel number of m is added before convolution and deconvolution in step (2.2), the same operation as in step (2.2) is carried out, G times of repetition is carried out to obtain high-level feature vector group generated by low-level features
Figure FDA0002396260890000021
And corresponding refined feature vector groups
Figure FDA0002396260890000022
(2.4) in order that the feedback input at the next moment contains better characteristics, fusing the refined characteristics through 1-layer convolution layers with the convolution kernel size of 1 × 1 and the convolution kernel number of m to generate the output of the feedback network
Figure FDA0002396260890000023
Step (3), deconvoluting the output of the feedback network, then obtaining a residual image by convolution, and adding an image obtained by up-sampling the original image through bilinear interpolation to the residual image to obtain a super-resolution image; the method comprises the following specific steps:
(3.1) feeding back the output of the network
Figure FDA0002396260890000024
Generating a high-resolution characteristic diagram through m deconvolution layers with convolution kernel size of k; drawing the original
Figure FDA0002396260890000025
By bilinear interpolation to obtain oneA large resolution map
Figure FDA0002396260890000026
(3.2) obtaining a residual image by passing the high-resolution feature map through a convolution layer with convolution kernel size of 3 × 3 of n layers
Figure FDA0002396260890000027
Finally, the large resolution map is processed
Figure FDA0002396260890000028
And residual image
Figure FDA0002396260890000029
Adding to obtain super-resolution image
Figure FDA00023962608900000210
When the image is a gray scale image, n is 1, and when the image is a color image, n is 3.
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