Image super-resolution reconstruction method and system based on convolutional neural network

A technology of super-resolution reconstruction and convolutional neural network, which is applied in the field of image super-resolution reconstruction based on convolutional neural network, can solve problems such as gradient disappearance, gradient explosion, and increased calculation, so as to reduce reconstruction time and reconstruct Improved quality, dramatic results

Inactive Publication Date: 2019-08-23
XIAMEN UNIV
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0004] Although the reconstruction method based on deep learning can improve the reconstruction performance of the algorithm by deepening the number of network layers, problems such as gradient disappearance and gradient explosion will appear as the number of network layers deepens, and the deepening of the network will also lead to an increase in the amount of calculation. large, these will reduce the quality of image reconstruction

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  • Image super-resolution reconstruction method and system based on convolutional neural network
  • Image super-resolution reconstruction method and system based on convolutional neural network
  • Image super-resolution reconstruction method and system based on convolutional neural network

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Embodiment 1

[0056] figure 1 It is a flowchart of the image super-resolution reconstruction method based on convolutional neural network in the present invention. like figure 1 Shown, a kind of image super-resolution reconstruction method based on convolution neural network, described method adopts seven network layers, and described network layer comprises first network layer, second network layer, the 3rd network layer, the 4th network layer layer, fifth network layer, sixth network layer and seventh network layer, including:

[0057] Step 101: Obtain an image to be reconstructed.

[0058] Step 102: Perform preprocessing on the image to be reconstructed to obtain a preprocessed image, specifically including:

[0059] Perform color space conversion on the image to be reconstructed to obtain a preprocessed brightness channel image.

[0060] Step 103: Extracting the features of the preprocessed image to obtain a feature image set, specifically including:

[0061] The preprocessed image...

Embodiment 2

[0071] figure 2 It is the structural diagram of the image super-resolution reconstruction system based on the convolutional neural network of the present invention. like figure 2 Shown, a kind of image super-resolution reconstruction system based on convolution neural network, described system adopts seven network layers, and described network layer comprises first network layer, second network layer, the 3rd network layer, the 4th network layer layer, fifth network layer, sixth network layer and seventh network layer, including:

[0072] The acquiring module 201 is configured to acquire an image to be reconstructed.

[0073] The preprocessing module 202 is configured to preprocess the image to be reconstructed to obtain a preprocessed image.

[0074] The feature extraction module 203 is configured to extract features of the preprocessed image to obtain a feature image set.

[0075] The mapping module 204 is configured to perform nonlinear mapping on the feature image se...

Embodiment 3

[0090] Embodiment 3 of the present invention constructs a network structure including three network layers including a convolutional layer, a residual network layer, and a sub-pixel convolutional layer. After the initial low-resolution image is preprocessed, the Y-channel image is used as the input data of the sub-pixel convolutional neural network, and the reconstruction is completed through continuous training of the network model. The specific parameter settings of the network model proposed by the present invention include 7 network layers in total, and each layer of network is defined as Conv (input, output, filter), where input is the number of input channels, output is the number of output channels, and filter is the volume The size of the core.

[0091] image 3 It is a network structure model diagram of the present invention. like image 3 As shown, in the feature extraction stage, both Conv1 and Conv2 use 64 5×5 convolution kernels to enrich the low-dimensional fe...

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Abstract

The invention discloses an image super-resolution reconstruction method and system based on a convolutional neural network. The method comprises the steps of obtaining a to-be-reconstructed image; preprocessing the to-be-reconstructed image to obtain a preprocessed image; extracting features of the preprocessed image to obtain a feature image set; performing nonlinear mapping on the feature imageset to obtain a global feature mapping set; and performing reconstruction according to the global feature mapping set to obtain a reconstructed high-resolution image. By adopting the method or the system, the reconstruction quality of the image can be improved.

Description

technical field [0001] The present invention relates to the technical field of image resolution reconstruction, in particular to an image super-resolution reconstruction method and system based on a convolutional neural network. Background technique [0002] With the advent of the digital age, image-based information processing methods have been widely used in various scenarios. The level of image resolution can affect the degree of perfection of information acquisition, and high-resolution images can provide more data information. In recent years, multiple network models based on deep learning have greatly improved the accuracy and computational performance in the field of image super-resolution reconstruction compared with traditional methods. However, the low-resolution images in these network models are usually enlarged into high-resolution images of the same size as the target image by using the bicubic interpolation method in the preprocessing stage. This means that ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T3/40G06N3/04
CPCG06T3/4076G06N3/045
Inventor 邵桂芳李铁军王颖刘暾东张俊发高凤强
Owner XIAMEN UNIV
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