Image super-resolution reconstruction method based on genetic algorithm and regular prior model

A priori model and genetic algorithm technology, applied in the field of image super-resolution reconstruction, can solve the problems that affect the image reconstruction effect and the large randomness of the high-resolution image to be estimated, and achieve the effect of improving the reconstruction effect

Active Publication Date: 2015-03-11
XIDIAN UNIV
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Problems solved by technology

In recent years, there have also been methods of using genetic algorithms to reconstruct super-resolution images, but because there is no guidance from any texture and edge information of the image, the randomness of the obtained high-resolution image to be estimated is too large, which affects the reconstruction effect of the image

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  • Image super-resolution reconstruction method based on genetic algorithm and regular prior model
  • Image super-resolution reconstruction method based on genetic algorithm and regular prior model
  • Image super-resolution reconstruction method based on genetic algorithm and regular prior model

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Embodiment Construction

[0030] Combine below figure 1 The specific implementation steps of the present invention are further described in detail.

[0031] Step 1, learning to obtain 200 sub-dictionaries from natural images.

[0032] Input 5 natural images, obtain image blocks containing a large amount of edge and structure information from these 5 natural images, use K-means to divide these image blocks into 200 categories, and use principal component analysis PCA to obtain a sub-dictionary from each category Φ k .

[0033] Step 2: Obtain a high-resolution image Xs, and extract the brightness component of Xs to initially estimate X.

[0034] (2a) Input the low-resolution image LR, and use bicubic interpolation to enlarge it by 3 times to obtain the initial estimate Xs of the high-resolution image;

[0035] (2b) Convert the initial estimate Xs of the high-resolution image from the red, green, blue RGB space to the YCbCr color space, and obtain the initial estimate Y of the brightness component, th...

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Abstract

The invention discloses an image super-resolution reconstruction method based on a genetic algorithm and a regular prior model, and mainly solves a problem of poor quality of a reconstruction result of a traditional method. The image super-resolution reconstruction method comprises the following implementation steps: (1) learning one group of sub-dictionaries from a natural image; (2) obtaining the luminance component estimation X of a high-resolution image Xs after a low-resolution (LR) image is magnified through interpolation by three times; (3) constructing an initial population; (4) calculating the fitness value of each individual; (5) selecting and copying the individuals in a parent population; (6) successively carrying out cross and variation to the individuals of the parent population; (7) repeating the steps (5) and (6) for twenty times to obtain an optimal solution X'; (8) carrying out local optimization on the X' by utilizing the regular prior model; and (9) repeating steps (3) to (8) for four times to obtain a luminance component X2 of a high-resolution image, and finally combining the high-resolution image. Image edges and texture information can be favorably kept, and the image super-resolution reconstruction method can be used for image identification and target classification.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an image super-resolution reconstruction method, which is used for super-resolution reconstruction of various natural images. Background technique [0002] In practical applications, due to the limitations of imaging equipment capabilities and the influence of complex working environment conditions, it is not easy to directly obtain high-quality images with the required resolution. Image super-resolution reconstruction SR technology breaks through the limitations of imaging equipment and environment, recovers high-frequency information lost during image acquisition from low-resolution observation images, and reconstructs a high-resolution image. Therefore, SR has very important application prospects in the fields of video, remote sensing, medicine and security monitoring, and has always been a hot spot in image science research and engineering applications. [0003] At ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/50G06T3/40G06N3/12
Inventor 李阳阳焦李成李亚肖马文萍尚荣华马晶晶杨淑媛侯彪
Owner XIDIAN UNIV
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