Recursive residual network-based super-resolution image reconstruction method

A technology of super-resolution reconstruction and image reconstruction, which is applied in image data processing, graphics and image conversion, biological neural network models, etc., can solve the problems of calculation, volume, power consumption limitation, deep neural network cannot be calculated and applied, etc. Achieve the effect of reducing computational complexity and reducing parameters

Inactive Publication Date: 2018-11-30
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

In practical applications, many such as mobile devices and embedded devices are limited in terms of calculation, volume, power consumption, e

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  • Recursive residual network-based super-resolution image reconstruction method
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  • Recursive residual network-based super-resolution image reconstruction method

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Embodiment Construction

[0025] In order to make the purpose, 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 in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0026] figure 1 It is a flowchart of a super-resolution image reconstruction method according to an embodiment of the present invention, such as figure 1 As shown, the method includes: inputting the low-resolution image into the trained recursive residual neural network to obtain a super-resolution reconstructed image, wherein the recursive resid...

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Abstract

Embodiments of the invention provide a recursive residual network-based super-resolution image reconstruction method. The method comprises the following step of: inputting a low-resolution image intoa trained recursive residual neural network so as to obtain a super-resolution reconstructed image, wherein the recursive residual neural network comprises a plurality of residual units, and for any residual unit, input information of the residual unit comprises output information of the last residual unit and a high-frequency feature image of a low-resolution input image. According to the method,the neural network is trained through local residual learning but not global residual learning of VDSR, so that benefit is brought to information transmission and gradient flow; non-interpolated low-resolution images are taken as inputs; and finally, super-resolution output images are directly up-sampled by using a convolution layer at the tail end of the network, and recursive structures are imported in the residual units, so that parameters are greatly decreased and the calculation complexity of the recursive residual neural network is reduced.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of image reconstruction, and in particular to a super-resolution image reconstruction method based on a recursive residual network. Background technique [0002] Single image super-resolution (Single image super-resolution, referred to as SISR) is a classic computer vision problem, aiming at recovering high-resolution (High-resolution) from a given low-resolution (Low-resolution, referred to as LR) image. -resolution, referred to as HR) image. Because SISR recovers high-frequency information, it is widely used in fields that require more image details, such as medical imaging, satellite imaging, security monitoring, etc. [0003] Existing super-resolution (SR) image reconstruction methods are mainly divided into three categories: interpolation-based SR technology, reconstruction-based SR technology and learning-based SR technology. Most of the current SR algorithms are learning-based ...

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Application Information

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IPC IPC(8): G06T3/40G06N3/04
CPCG06T3/4053G06N3/045
Inventor 赵丽娟周登文段然柴晓亮
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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