CN111402128A - Image super-resolution reconstruction method based on multi-scale pyramid network - Google Patents
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Abstract
The invention discloses an image super-resolution reconstruction method based on a multi-scale pyramid network, which comprises the following steps: s1, shallow feature extraction is carried out on the input image; s2, performing feature fusion and feature enhancement on the shallow features through K multi-scale residual error modules to obtain richer deep features; s3, performing up-sampling on the deep level features by using the transposed convolution; s4, reconstructing the image by residual error learning; and S5, taking the reconstructed image as the output of the current pyramid network and the input of the next pyramid network, and continuing to train by adopting the steps S1-S4 to obtain an image with higher resolution. The invention adopts a multi-scale residual error module to fuse the characteristics to obtain richer characteristics; meanwhile, a Laplacian pyramid network is adopted to gradually up-sample and reconstruct a high-resolution image; by the method, the image with richer details and higher quality can be reconstructed.
Description
Technical Field
The invention relates to the technical field of computer vision and image processing, in particular to an image super-resolution reconstruction method based on a multi-scale pyramid network.
Background
With the development of information technology, the number of pictures on a network is continuously increased, and the images are used as a main medium for people to learn the world and are applied to various scenes. In a plurality of fields, the image quality is as large as that of medical images, satellite remote sensing, and people's cameras, mobile phones and the like. People have increasingly high requirements on the image quality of images. Therefore, the improvement of the resolution of the image is of great significance in real life.
Image super-resolution reconstruction aims to recover a high-resolution image by using one or more low-resolution images, and has been developed in recent years as one of the research hotspots in the field of computer vision. At present, super-resolution reconstruction algorithms are divided into two categories, namely interpolation-based and learning-based, in the aspect of reconstruction algorithms. The interpolation-based algorithm is simple and fast, but cannot meet the increasing image quality requirements of people. The super-resolution reconstruction method based on learning learns the prior by means of extra training samples to reduce the ill-posed property of the super-resolution problem and obtain better effect, such as a method based on sparse coding and a method based on neighborhood embedding. However, these methods only solve sparse coding coefficients and learn embedding space on the primary feature space of the image, so that sparsity and manifold assumptions are difficult to strictly satisfy, directly resulting in degradation of image reconstruction quality. With the rapid development of deep learning, researchers widely apply the deep learning algorithm to image super-resolution reconstruction and obtain a reconstruction result superior to an interpolation algorithm. However, the mainstream methods at present are based on the theory that the deeper the network is, the better the reconstruction effect is, and with the increase of the network depth, the problems of gradient disappearance or grid degradation still exist, and most methods are to reach the specified size through one-time up-sampling, so that the quality of the reconstructed high-resolution image needs to be improved.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides an image super-resolution reconstruction method based on a multi-scale pyramid network.
The purpose of the invention can be achieved by adopting the following technical scheme:
an image super-resolution reconstruction method based on a multi-scale pyramid network comprises the following steps:
s1, shallow feature extraction is carried out on the input image;
s2, performing feature fusion and feature enhancement on the shallow features through K multi-scale residual error modules to obtain deep features;
s3, performing up-sampling on the deep level features by using the transposed convolution;
s4, reconstructing the image by residual error learning;
and S5, taking the reconstructed image as the output of the current pyramid network and simultaneously as the input of the next pyramid network, and continuously repeating the training from the step S1 to the step S4 to obtain an image with higher resolution.
Further, the step S1 is as follows:
extracting shallow features from the input low resolution image using a layer of 3 × 3 convolutional layer followed by a non-linear activation unit, the expression is as follows:
F0＝σ(W1*ILR) (1)
wherein, ILRRepresenting the input low resolution image, sigma represents the nonlinear activation function Re L U, W1Convolution kernel representing the convolution layer of 3 × 3, F0Representing features extracted by convolutional layers.
Further, each multi-scale residual module in S2 comprises a feature enhancement unit, a compression unit and a residual learning unit, wherein the feature enhancement unit comprises 2 convolution layers of 3 × 3 followed by nonlinear activation units and 2 convolution layers of 5 × 5 followed by nonlinear activation units, the compression unit comprises a layer of convolution layers with the size of 1 × 1, compared with a single-scale convolution kernel, features of different scales can be extracted by convolution kernels with different sizes, so that a filter can extract and learn richer image information.
Further, the step S2 is as follows:
firstly, the shallow layer features extracted in the step S1 are processed by a feature enhancement unit to obtain two different features, then the two different features are processed by feature fusion by a compression unit, the fused features are further processed by learning of a convolution layer, and finally the fused features and the shallow layer features are added to form a residual block, wherein the expression of the calculation process is as follows:
M＝W1×1*[T2，P2](6)
B＝σ(W*M) (7)
Fm＝B+Fm-1(8)
wherein, T1Characteristic after lamination by the first layer 3 × 3, T2Characteristic of the post-lamination by the second layer 3 × 3, P1Characteristic of the post-lamination layer after lamination by the first layer 5 × 5, P2For the characteristics after the layer was wrapped by the second layer 5 × 5, σ represents the nonlinear activation function Re L U function,
the convolution kernel of the first layer 3 × 3 convolutional layer,
the convolution kernel of the second layer 3 × 3 convolutional layer,
the convolution kernel of the first layer 5 × 5 convolutional layer,
convolution kernel, W, of the second layer 5 × 5 convolution layer1×1Denotes a convolution kernel of 1 [1 × 1 ] convolution layer, W denotes a convolution kernel of the last learning layer, [ alpha ], [ alpha]Represents a feature fusion function, M represents a feature after fusion by 1 × 1 convolutional layers,Brepresenting features obtained by the last learning layer, Fm-1And FmRespectively representing the input and output of the mth multi-scale residual block.
Further, the step S3 is as follows:
using a layer of transposition convolution layer to up-sample the deep-level features passing through the K multi-scale residual error modules to obtain a high-resolution image, wherein the expression is as follows:
IHR_conv＝fdeconv(FK) (9)
in the above formula, IHR_convFor high resolution images after upsampling, fdeconvFor up-sampling operations, FKIs the output of the kth multi-scale residual module.
Further, the step S4 is as follows:
firstly, carrying out double cubic interpolation on a low-resolution image to obtain a high-resolution image IHR_bicuThen the high resolution image IHR_bicuWith up-sampled high-resolution image IHR_convAdding to obtain a depth map I with spatial resolution amplified by two timesHRThe expression is as follows:
IHR＝IHR_bicu+IHR_deconv(10)
further, compared with a network in which an image with a specified size is obtained by only one-time upsampling, the pyramid network can be used for gradual upsampling, so that the training difficulty of the network (especially the training of large-scale factors) can be reduced, and a picture with higher quality can be obtained. The pyramid network comprises N levels in total, if the input low-resolution image is a low-resolution image with the down-sampling rate of 1/S times, and S is an up-sampling scale factor, N is log2S; each stage reconstructs an image output by a previous stage into a high-resolution image of the stage.
Compared with the prior art, the invention has the following advantages and effects:
according to the invention, the multi-scale residual error module is adopted to extract various characteristics from the image, the characteristics are reinforced by fusing the characteristics, so that the extracted characteristics are richer, and the image is gradually up-sampled and reconstructed in a pyramid network mode, so that the high-resolution image quality is higher.
Drawings
FIG. 1 is a schematic diagram of an image super-resolution reconstruction method based on a multi-scale pyramid network disclosed in the present invention;
fig. 2 is a multi-scale residual module framework diagram in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
As shown in fig. 1, the present embodiment provides an image super-resolution reconstruction method based on a multi-scale pyramid network, which performs fusion and reinforcement on extracted features through a multi-scale residual module, and gradually performs up-sampling on the pyramid network to gradually reconstruct an image, and specifically includes the following steps:
s1, shallow feature extraction is carried out on the input image, and the method specifically comprises the following steps:
shallow features are extracted from the input low resolution image using a layer of 3 × 3 convolutional layer followed by a non-linear activation unit, as follows:
F0＝σ(W1*ILR) (1)
wherein, ILRRepresenting the input low resolution image, sigma represents the nonlinear activation function Re L U, W1Convolution kernel representing the convolution layer of 3 × 3, F0Representing features extracted by convolutional layers.
S2, performing feature fusion and feature enhancement on the shallow features through a plurality of K multi-scale residual error modules to obtain deep features, specifically:
the multi-scale residual error module can extract features of different scales by using convolution kernels of different sizes, so that the filter can extract and learn richer image information. As shown in fig. 1, by inputting the shallow feature into K multi-scale residual error modules, the shallow feature can be enhanced to obtain richer and deeper features, where K is 2 in this embodiment, but the value of K does not limit the technical solution of the present invention.
Multi-scale residual modules as shown in fig. 2, each multi-scale residual module comprises a feature enhancement unit, a compression unit and a residual learning, wherein the feature enhancement unit comprises 2 convolutional layers of 3 × 3 followed by nonlinear activation units and 2 convolutional layers of 5 × 5 followed by nonlinear activation units, the compression unit is composed of one layer of convolutional layer with the size of 1 × 1, and the utilization of the residual learning makes the network easier to optimize.
Firstly, the shallow layer features extracted in the step S1 are processed by a feature enhancement unit to obtain two different features, then the two different features are processed by feature fusion by a compression unit, the fused features are further processed by learning of a convolution layer, and finally the feature is added with the shallow layer features to form a residual block; the expression is as follows:
M＝W1×1*[T2，P2](6)
B＝σ(W*M) (7)
Fm＝B+Fm-1(8)
wherein, T1Characteristic after lamination by the first layer 3 × 3, T2Characteristic of the post-lamination by the second layer 3 × 3, P1Characteristic of the post-lamination layer after lamination by the first layer 5 × 5, P2For the characteristics after the layer was wrapped by the second layer 5 × 5, σ represents the nonlinear activation function Re L U function,
the convolution kernel of the first layer 3 × 3 convolutional layer,
the convolution kernel of the second layer 3 × 3 convolutional layer,
the convolution kernel of the first layer 5 × 5 convolutional layer,
convolution kernel, W, of the second layer 5 × 5 convolution layer1×1Denotes a convolution kernel of 1 [1 × 1 ] convolution layer, W denotes a convolution kernel of the last learning layer, [ alpha ], [ alpha]Represents a feature fusion function, M represents a feature after fusion by 1 × 1 convolutional layers,Brepresenting features obtained by the last learning layer, Fm-1And FmRespectively representing the input and output of the mth multi-scale residual block. In this embodiment, m is [1,2 ]]。
S3, using a layer of transposition convolution layer to up-sample the deep-level features passing through the 2 multi-scale residual modules to obtain a high-resolution image, wherein the expression is as follows:
IHR_conv＝fdeconv(F2) (9)
in the above formula, IHR_convFor high resolution images after upsampling, fdeconvFor up-sampling operations, F2Is the output of the 2 nd multi-scale residual module.
S4, reconstructing the image by residual learning, specifically:
firstly, carrying out double cubic interpolation on a low-resolution image to obtain a high-resolution image IHR_bicuThen the high resolution image IHR_bicuWith up-sampled high-resolution image IHR_convAdding to obtain a depth map I with spatial resolution amplified by two timesHRThe expression is as follows:
IHR＝IHR_bicu+IHR_deconv(10)
and S5, taking the reconstructed image as the output of the current pyramid network layer and the input of the next pyramid network layer, and continuing to adopt the training from the step S1 to the step S4 to obtain an image with higher resolution.
The pyramid network comprises N levels in total, if the input low-resolution image is a down-sampling 1/S times low-resolution image, and S is an up-sampling scale factor, N is log2S; each stage reconstructs an image output by a previous stage into a high-resolution image of the stage.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (7)
1. The image super-resolution reconstruction method based on the multi-scale pyramid network is characterized by comprising the following steps of:
s1, shallow feature extraction is carried out on the input image;
s2, performing feature fusion and feature enhancement on the shallow features through K multi-scale residual error modules to obtain deep features;
s3, performing up-sampling on the deep level features by using the transposed convolution;
s4, reconstructing the image by residual error learning;
and S5, taking the reconstructed image as the output of the current pyramid network and simultaneously as the input of the next pyramid network, and continuously repeating the training from the step S1 to the step S4 to obtain an image with higher resolution.
2. The method for reconstructing image resolution based on multi-scale pyramid network as claimed in claim 1, wherein said step S1 is performed as follows:
extracting shallow features from the input low resolution image using a layer of 3 × 3 convolutional layer followed by a non-linear activation unit, the expression is as follows:
F0＝σ(W1*ILR) (1)
wherein, ILRRepresenting the input low resolution image, sigma represents the nonlinear activation function Re L U, W1Convolution kernel representing the convolution layer of 3 × 3, F0Representing features extracted by convolutional layers.
3. The method as claimed in claim 1, wherein each multi-scale residual error module comprises a feature enhancement unit, a compression unit and a residual error learning, wherein the feature enhancement unit comprises 2 convolutional layers of 3 × 3 followed by nonlinear activation units and 2 convolutional layers of 5 × 5 followed by nonlinear activation units, and the compression unit comprises a convolutional layer with a size of 1 × 1.
4. The method for reconstructing image resolution based on multi-scale pyramid network as claimed in claim 3, wherein said step S2 is as follows:
firstly, the shallow layer features extracted in the step S1 are processed by a feature enhancement unit to obtain two different features, then the two different features are processed by feature fusion by a compression unit, the fused features are further processed by learning of a convolution layer, and finally the fused features and the shallow layer features are added to form a residual block, wherein the expression of the calculation process is as follows:
M＝W1×1*[T2，P2](6)
B＝σ(W*M) (7)
Fm＝B+Fm-1(8)
wherein, T1Characteristic after lamination by the first layer 3 × 3, T2Characteristic of the post-lamination by the second layer 3 × 3, P1Characteristic of the post-lamination layer after lamination by the first layer 5 × 5, P2For the characteristics after the layer was wrapped by the second layer 5 × 5, σ represents the nonlinear activation function Re L U function,
the convolution kernel of the first layer 3 × 3 convolutional layer,
the convolution kernel of the second layer 3 × 3 convolutional layer,
the convolution kernel of the first layer 5 × 5 convolutional layer,
convolution kernel, W, of the second layer 5 × 5 convolution layer1×1Denotes a convolution kernel of 1 [1 × 1 ] convolution layer, W denotes a convolution kernel of the last learning layer, [ alpha ], [ alpha]Represents a feature fusion function, M represents a feature after fusion by 1 × 1 convolutional layers, B represents a feature obtained by the last learning layer, and Fm-1And FmRespectively representing the input and output of the mth multi-scale residual block.
5. The method for reconstructing image resolution based on multi-scale pyramid network as claimed in claim 1, wherein said step S3 is performed as follows:
using a layer of transposition convolution layer to up-sample the deep-level features passing through the K multi-scale residual error modules to obtain a high-resolution image, wherein the expression is as follows:
IHR_conv＝fdeconv(FK) (9)
in the above formula, IHR_convFor high resolution images after upsampling, fdeconvFor up-sampling operations, FKIs the output of the kth multi-scale residual module.
6. The method for reconstructing image resolution based on multi-scale pyramid network as claimed in claim 1, wherein said step S4 is performed as follows:
firstly, carrying out double cubic interpolation on a low-resolution image to obtain a high-resolution image IHR_bicuThen the high resolution image IHR_bicuWith up-sampled high-resolution image IHR_convAdding to obtain a depth map I with spatial resolution amplified by two timesHRThe expression is as follows:
IHR＝IHR_bicu+IHR_deconv(10)
7. the method as claimed in any one of claims 1 to 6, wherein the pyramid network comprises a total of N levels, and if the input low-resolution image is a down-sampled 1/S times low-resolution image and S is an up-sampling scale factor, then N is log2S; each stage reconstructs an image output by a previous stage into a high-resolution image of the stage.
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