Resolution-by-resolution improved image super-resolution restoration method based on an attention mechanism

A super-resolution and attention technology, applied in image analysis, image enhancement, image data processing and other directions, can solve problems such as inability to handle multi-scale problems

Pending Publication Date: 2020-07-28
BEIJING UNIV OF TECH
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Problems solved by technology

However, such methods have a disadvantage: they cannot handle multi-scale problems in a single framework

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  • Resolution-by-resolution improved image super-resolution restoration method based on an attention mechanism
  • Resolution-by-resolution improved image super-resolution restoration method based on an attention mechanism
  • Resolution-by-resolution improved image super-resolution restoration method based on an attention mechanism

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Embodiment Construction

[0033] Embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0034] The overall structure of the present invention is as figure 1 shown. A low-resolution image of 480×480 is obtained after downsampling using bicubic linear interpolation. Input low-resolution images into the designed network consisting of convolutional layers and residual blocks to learn the mapping relationship. First, the network model uses a 3×3 deformable convolution for feature extraction of information. The activation unit is RELU, which is used to reduce the interdependence of parameters and solve the occurrence of overfitting problems. Then, four iterative basic block units are designed, and the feature dimensions are 64, 128, and 512 respectively. The block unit is mainly composed of two deformable convolutions and SElayer operations, which can be more conducive to extracting the edge features of the target. Finally, shuffle is use...

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Abstract

The invention discloses a resolution-by-resolution improved image super-resolution restoration method based on an attention mechanism, which is mainly based on a resolution-by-resolution improved super-resolution restoration network of a double attention mechanism, and improves the feature extraction capability of a model by introducing a convolution module of a feature dimension attention mechanism and a spatial dimension attention mechanism. Furthermore, related ideas of a resolution-by-resolution improvement network in the field of adversarial neural networks are used for reference, the network learning difficulty is simplified, and super-resolution restoration of resolution-by-resolution improvement is realized. Finally, the algorithm performance is tested through a DIV2K data set, andexperimental results show that the method can improve the input low-resolution image resolution by resolution, the same network can improve the resolution by 2-4 times at the same time, and the PSNRvalue of the reconstructed image is significantly superior to that of a current mainstream algorithm.

Description

technical field [0001] The present invention belongs to the research hotspot of super-resolution restoration in the field of image processing. It is a method of using a double attention mechanism to upgrade the super-resolution restoration network by resolution to obtain a single-frame image with higher resolution, which is different from the current mainstream Based on the convolutional neural network of SRCNN, VDSR and SRResNet, this method improves the feature extraction ability of the model; simplifies the difficulty of network learning, and realizes super-resolution restoration with resolution-by-resolution improvement; it has been well verified on public datasets. Background technique [0002] The super-resolution restoration of images, especially the super-resolution restoration of single-frame images, has received more and more attention and research in recent years. Its goal is to reconstruct a high-resolution image from a single low-resolution image. Typically, th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T3/40G06T5/00G06N3/04G06N3/08
CPCG06T3/4076G06N3/08G06T2207/20081G06T2207/20084G06N3/045G06T5/00
Inventor 王素玉张雨婷
Owner BEIJING UNIV OF TECH
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