Image super-resolution reconstruction method based on multi-column convolution neural network

A convolutional neural network and super-resolution reconstruction technology, applied in biological neural network model, neural architecture, image data processing and other directions, can solve the problems of poor reconstruction ability, weak robustness, poor visual effect, etc. Rebuild speed, reduction in computation, effect of reduction in computation

Pending Publication Date: 2019-01-22
SHANGHAI UNIV
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Problems solved by technology

[0004] Although the proposed image super-resolution algorithm based on convolutional neural network solves the problems of poor robustness and computational complexity in traditional image super-resolution reconstruction algorithms, the existing image super-resolution algorithm based on convolutional neural network Before extracting the features of the low-resolution image, the low-resolution image must b

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  • Image super-resolution reconstruction method based on multi-column convolution neural network
  • Image super-resolution reconstruction method based on multi-column convolution neural network
  • Image super-resolution reconstruction method based on multi-column convolution neural network

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[0025] The preferred embodiments of the present invention are detailed as follows with reference to the drawings:

[0026] The structure of the multi-column convolutional neural network of this embodiment is as follows figure 1 Shown. In Ubuntu 16.04, PyTorch environment programming simulation to achieve this method. First, design a multi-column convolutional neural network model according to the deep learning algorithm, including feature extraction part and image reconstruction part. Then, the original image is cut into small blocks, and these high-resolution small blocks are down-sampled to obtain low-resolution small blocks, and these low-resolution and high-resolution small block pairs are used to build a training set. Finally, the stochastic gradient descent algorithm is used to train this model to obtain a model that reconstructs a low-resolution image to a high-resolution image, that is, the image super-resolution reconstruction model of the multi-column convolutional neu...

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Abstract

The invention discloses an image super-resolution reconstruction method based on a multi-column convolution neural network. Firstly, a multi-column convolution neural network model is designed according to the depth learning algorithm, and includes feature extraction and image reconstruction. Then, the original image is cut into small blocks, and these high-resolution blocks are downsampled to getlow-resolution blocks, and these low-resolution and high-resolution pairs of blocks are used to build the training set. Finally, the model is trained by using the stochastic gradient descent algorithm to get a model that reconstructs the low-resolution image to the high-resolution image, and the input low-resolution image is reconstructed to the corresponding high-resolution image. The method ofthe invention is tested on five universal image databases of Set5, Set14, BSDS100, Urban100 and Manga109, and has high robustness and accuracy.

Description

technical field [0001] The invention relates to an image super-resolution reconstruction method, in particular to a super-resolution reconstruction method based on multi-column convolutional neural network images, which belongs to the utilization of image processing and reconstruction technologies. Background technique [0002] With the development of information technology, images, as the main medium of information dissemination, have been widely used in various scenarios. In many fields, people have high requirements for image quality, so for the rapidly developing information age, it is difficult for low-quality images to meet the needs of specific scenes. Image resolution is an important indicator for measuring image quality. The higher the image resolution, the more detailed information the image contains. Image super-resolution (Super-Resolution, SR) reconstruction belongs to image processing technology, which reconstructs high-resolution (High-Resolution, HR) images ...

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Application Information

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IPC IPC(8): G06T3/40G06N3/04
CPCG06T3/4053G06N3/045Y02T10/40
Inventor 王永芳帅源
Owner SHANGHAI UNIV
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