Image super-resolution reconstruction method based on fused attention mechanism residual network

A super-resolution reconstruction and attention technology, applied in the field of image processing, can solve the problems of limited dictionary learning linear representation ability, slow convergence speed, lack of attention to high-frequency details, etc., to save training time and computing overhead, pixels Uniform distribution of data values, improved detail and clarity

Inactive Publication Date: 2020-05-22
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

Including dictionary-based learning methods, using sparse coding, but limited by the linear representation ability of dictionary learning, the reconstruction effect is limited
In recent years, with the application of convolutional neural network (CNN) in the field of computer vision, representative network models such as SRCNN have emerged in super-resolution reconstruction methods, but these reconstruction methods also have some problems, such as the lack of Attention to high-frequency details, slow convergence, long training time, etc.
[0007] After searching, it is found that the Chinese patent application number 201811439902X discloses a method for super-resolution reconstruction of remote sensing images based on channel attention convolutional neural network. Although the network model of this method is also composed of feature extraction layer, feature learning layer, image Reconstruction layer composition, but its feature extraction layer only uses one layer of convolution operation to extract shallow features, only the channel attention mechanism is introduced in the residual module structure of the feature learning layer, and the overall connection structure draws on the recursive network structure, but there is no Ensure that

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  • Image super-resolution reconstruction method based on fused attention mechanism residual network
  • Image super-resolution reconstruction method based on fused attention mechanism residual network
  • Image super-resolution reconstruction method based on fused attention mechanism residual network

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[0058] The technical solution of the present invention is described in further detail below in conjunction with the accompanying drawings: the present embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation methods and specific operating procedures are provided, but the protection authority of the present invention does not Limited to the following examples.

[0059] This embodiment proposes an image super-resolution reconstruction method based on the fusion attention mechanism residual network, such as figure 1 shown, including the following steps:

[0060] S1. Data collection and preprocessing.

[0061] S11. Separately collect image data sets for training and image data sets to be reconstructed. In this embodiment, the standard public data set DIV2K is used as the training data set, including 800 training images, 100 verification images and 100 test images. The dataset to be reconstructed uses other standard ...

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Abstract

The invention provides an image super-resolution reconstruction method based on a fused attention mechanism residual network, and solves the problems of poor image reconstruction quality and non-idealvisual effect in the prior art. The researched image reconstruction method comprises the steps of S1, performing data acquisition and preprocessing, and obtaining a training image data set and a to-be-reconstructed image data set; S2, building a network model, wherein the model structure comprises a feature extraction layer, a feature learning layer and an image reconstruction layer; S3, initializing, training and storing model parameters to obtain an optimal model structure and an optimal parameter set; and S4, performing image super-resolution reconstruction, inputting a to-be-reconstructedimage, and outputting a high-resolution image under a corresponding amplification scale. The image super-resolution network provided by the invention combines global and local residual structures, fuses channel attention and spatial attention mechanisms, pays more attention to high-frequency information of the image, reserves important features in the image to the greatest extent, reduces repeated and redundant features, and greatly improves the details and definition of the reconstructed image.

Description

technical field [0001] The invention relates to an image super-resolution reconstruction method, in particular to an image super-resolution reconstruction method based on a fusion attention mechanism residual network, and belongs to the technical field of image processing. Background technique [0002] Image resolution indicates the amount of information stored in an image, usually expressed as "horizontal pixel value × vertical pixel value", and is an important indicator to measure image quality. Generally, the higher the resolution of an image, the richer the details it contains, the more information it provides, and the better the image quality. Therefore, images with higher resolution have important application value and importance in various computer vision tasks. Research prospects. However, due to cost issues, it is usually unavoidable to be subject to conditional restrictions or other noise interference during image acquisition, storage, and transmission in reality,...

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Application Information

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IPC IPC(8): G06T3/40
CPCG06T3/4046G06T3/4053
Inventor 陈靖章韵
Owner NANJING UNIV OF POSTS & TELECOMM
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