Image super-resolution reconstruction method based on back-propagation neural network

A super-resolution reconstruction, neural network technology, applied in image enhancement, image data processing, graphics and image conversion, etc., can solve problems such as low image resolution

Inactive Publication Date: 2017-06-13
SHENZHEN WEITESHI TECH
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Problems solved by technology

[0004] For the problem of low resolution of synthetic aperture radar images, the object of the present in...

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  • Image super-resolution reconstruction method based on back-propagation neural network
  • Image super-resolution reconstruction method based on back-propagation neural network
  • Image super-resolution reconstruction method based on back-propagation neural network

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Embodiment Construction

[0044] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present invention will be further described in detail below in conjunction with the drawings and specific embodiments.

[0045] figure 1 It is a system flowchart of an image super-resolution reconstruction method based on a backpropagation neural network in the present invention. It mainly includes image input; preprocessing; image denoising; maintaining image edges; backpropagation neural network.

[0046] Wherein, in the image input, remote sensing images obtained by synthetic aperture radar are selected as input data; remote sensing images are images of targets on the ground, including features obtained from different angles and heights; synthetic aperture radar images are full of multiplicative spots Noise, and limited by the imaging device, the resolution is low.

[0047] Among them, in the prepro...

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Abstract

The invention discloses an image super-resolution reconstruction method based on a back-propagation neural network. The contents of the method mainly include image input, preprocessing, image denoising, image-border maintaining and the back-propagation neural network. The method includes the steps that first, a synthetic aperture radar image is preprocessed, multiplicative noise of the image is converted into additive noise, an improved non-local mean value is adopted to conduct the denoising, and an exponentiation operation is adopted to restore the image; then, a kernel function is adopted to maintain clear borders of a reduced image; finally, through the treatment of the back-propagation neural network, a super-resolution reconstruction result is obtained. According to the method, a neural network model is adopted, and a large number of computing resources and a large amount of calculation time are saved; according to the improved non-local mean value method, a low-resolution image is reconstructed into a high-resolution image, speckle noise of the synthetic aperture radar image is denoised, and the combination of the modified non-local mean value method and the back-propagation neural network greatly improves the reconstruction effect.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image super-resolution reconstruction method based on a backpropagation neural network. Background technique [0002] With the rapid development of science and technology, remote sensing images acquired through synthetic aperture radar systems play an important role in disaster monitoring, environmental monitoring, resource exploration and other fields because they can eliminate the interference of weather and seasons. However, due to the limitations of the imaging device, only low-resolution images can be obtained. In addition, SAR images are seriously affected by speckle noise, and many existing super-resolution reconstruction algorithms cannot obtain a high accuracy and good performance when processing such images. However, if the image super-resolution reconstruction method based on the backpropagation neural network is used, the fusion of the improved non-local mean metho...

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Application Information

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IPC IPC(8): G06T3/40G06T5/00
CPCG06T3/4053G06T5/002
Inventor 夏春秋
Owner SHENZHEN WEITESHI TECH
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