Fuzzy image super-resolution reconstruction method based on fractional differential

A technology of super-resolution reconstruction and fractional differentiation, which is applied in image data processing, graphics and image conversion, instruments, etc., can solve the problems of application limitation of calculation amount, information loss, and small amount of calculation, etc., and achieve high degree of freedom and adaptability performance, good reconstruction results, and improved adaptability

Active Publication Date: 2017-12-01
河南宝通信息安全测评有限公司
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[0003] At present, image super-resolution algorithms can be mainly divided into three categories: methods based on interpolation theory, methods based on learning, and methods based on reconstruction and enhancement theory. It will be accompanied by a large amount of information loss and blurring, which cannot normally achieve the goal in super-resolution of blurred ...

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  • Fuzzy image super-resolution reconstruction method based on fractional differential
  • Fuzzy image super-resolution reconstruction method based on fractional differential

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[0030] The technical solutions of the present invention will be described in further detail below through specific implementation methods.

[0031] A fuzzy image super-resolution reconstruction method based on fractional differential, comprising the following steps:

[0032] Step 1, sequentially perform Gaussian blur and downsampling on the original image set to obtain the input image sequence;

[0033] Step 1.1, generate Gaussian filters of different sizes (such as 3×3, 5×5) and different standard deviations (such as 0.1, 0.5, 0.9), and use the Gaussian filter to perform Gaussian filtering on the images in the original image set to obtain blur image collection;

[0034] Step 1.2, perform 0.5 times down-sampling on each frame image in the simulated image set, and obtain 4 low-resolution images that are different from each other, and all low-resolution images constitute the input image sequence;

[0035] Step 2, select any frame of input image from the input image sequence to...

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Abstract

The invention provides a fuzzy image super-resolution reconstruction method based on fractional differential. The method comprises the steps of (1) carrying out Gauss fuzzy and down sampling processing on an original image set in order, and obtaining an input image sequence, (2) selecting one arbitrary frame of input image from the input image sequence to carry out bicubic interpolation amplification, and obtaining an original reference frame, (3) using an adaptive fractional differential algorithm to carrying out image enhancement processing on each frame of input image in the input image sequence and the original reference frame, (4) calculating a motion matrix between each frame of input image and the original reference frame through SIFT matching, and searching a point in each frame of input image corresponding to a point in the original reference frame, and (5) calculating a residual error between the point in the original reference frame and the corresponding point in each frame of input image, through residual error inverse iterative projection correction, and the pixel value of the point in the original reference frame is constantly adjusted until a preset condition is satisfied.

Description

technical field [0001] The invention relates to an image super-resolution reconstruction method, in particular to a fractional differential-based fuzzy image super-resolution reconstruction method. Background technique [0002] With the development of computer technology and machine vision technology, the method of image super-resolution has made great progress, and has important applications in many fields, such as multimedia, security monitoring and medical fields. [0003] At present, image super-resolution algorithms can be mainly divided into three categories: methods based on interpolation theory, methods based on learning, and methods based on reconstruction and enhancement theory. It will be accompanied by a large amount of information loss and blurring, which cannot normally achieve the goal in super-resolution of blurred images. Although the effect of image super-resolution based on learning is good, it relies heavily on external databases. Without a good and suff...

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Application Information

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IPC IPC(8): G06T3/40
CPCG06T3/4023G06T3/4053
Inventor 陈长宝杜红民侯长生孔晓阳王茹川郭振强郧刚王磊王莹莹肖进胜
Owner 河南宝通信息安全测评有限公司
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