An image super-resolution reconstruction method

A super-resolution reconstruction and low-resolution technology, which is applied in the field of image super-resolution reconstruction and image processing, can solve problems such as supplementary pixel errors, and achieve the effect of saving storage space

Active Publication Date: 2019-05-07
四川康吉笙科技有限公司
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Problems solved by technology

This method relies too much on the sample library without introducing some basic principles of human cognition to images. However, due to the complexity and diversity of images, it is difficult for the sample library to ensure sufficient distribution in various image details. In this case The model trained in the next step should also emphasize the sharpness of the image, that is, it is exactly the same as the original image, and it is easy to cause obvious errors in supplementary pixels.

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Embodiment Construction

[0021] The specific embodiment of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0022] Image super-resolution reconstruction method described in the present invention, comprises the steps:

[0023] Making a learning sample set: a large number of high-resolution original images with the same pixel size A 1 , A 2 ,…A M After N times of smoothing respectively, the original size images with different degrees of blur are generated {(A 10 A 11 A 12 …A 1N ), (A 20 A 21 A 22 …A 2N )…(A M0 A M1 A M2 …A MN )}, the subscript 0,1,2...N indicates the number of smoothing operations, the larger the value, the blurrier the image, and 0 corresponds to the original image. 1, 2, ... M represent different high-resolution original images, M is the number of pictures in the sample set, and the different blur images generated by each image together form a label image, while the original image is reduced to form ...

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Abstract

The invention discloses an image super-resolution reconstruction method. The method comprises the following steps: making a learning sample set; Learning and training: a single-frame model of the convolutional neural network is adopted as a recurrent network module, a super-resolution mapping model of a recurrent neural network mode comprising N + 1 recurrent network modules is constructed, and inthe training process, a single lowest-resolution image is input into each recurrent network module in the super-resolution mapping model for single-time training; Replacing a low-resolution picture and repeatedly carrying out the single training; Carrying out single training for multiple times; And reconstruction: inputting the low-resolution image to be reconstructed into the trained super-resolution mapping model to obtain a high-resolution image. According to the method, the model is guided to learn low-frequency and high-frequency information of the image at the same time by adding the output error items related to the blurred image in the performance indexes, so that the image super-resolution reconstruction result can give consideration to reliability and details; And meanwhile, thestorage space can be greatly saved through the super-resolution mapping model in a cyclic network form.

Description

technical field [0001] The invention belongs to the technical field of software and relates to image processing technology, in particular to an image super-resolution reconstruction method. Background technique [0002] Image super-resolution reconstruction technology is a means of generating high-resolution images from low-resolution images, and has great application value in medical images, satellite photography, security monitoring and other fields. Super-resolution reconstruction techniques can be divided into three categories: interpolation-based methods, model-based methods, and learning-based methods. Among them, the learning-based method is the current mainstream direction, especially with the continuous development of deep learning technology and convolutional neural network, the image quality of super-resolution reconstruction is also improving. It learns image features and a low-resolution to high-resolution mapping model through a large number of pairs of low-re...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T3/40
CPCY02T10/40
Inventor 吴钦章李俊
Owner 四川康吉笙科技有限公司
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