High-resolution dictionary based sparse representation image super-resolution reconstruction method

A high-resolution and high-resolution technology, applied in the field of image processing, can solve the problems of large difference in reconstruction results of blocks with similar structures, unclear edges and textures of reconstructed images, and the need to relearn, etc., to achieve the effect of enriching texture details

Inactive Publication Date: 2011-08-03
XIDIAN UNIV
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Problems solved by technology

[0004] To sum up, although the existing technology of super-resolution reconstruction based on instance learning can effectively realize the super-resolution reconstruction of a single frame image, the learned low-resolution-high-resolution data pair is only for a specific magnification, and the data Must be relearned as magnification changes
At the same time, the reconstruction results of the above two methods for the divided structurally simi

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  • High-resolution dictionary based sparse representation image super-resolution reconstruction method
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  • High-resolution dictionary based sparse representation image super-resolution reconstruction method

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specific Embodiment approach

[0057] refer to figure 1 , the specific embodiments of the present invention are as follows:

[0058] Step 1, build a high-resolution brightness image library:

[0059] 1a) Randomly download multiple color high-resolution natural images from the Internet;

[0060] 1b) Convert high-resolution natural images from red, green, blue RGB color space to brightness, blue chroma, red chroma YCbCr color space;

[0061] 1c) Collect all luminance images to build a high-resolution luminance image library.

[0062] Step 2, generate a sample training set based on the brightness image library:

[0063] 2a) dividing all luminance images in the high-resolution luminance image library into square image blocks;

[0064] 2b) Select 50,000 square image blocks of 7×7, and rotate the selected 50,000 square image blocks by 90 degrees;

[0065] 2c) Representing all square image blocks before and after rotation with column vectors;

[0066] 2d) Collect all column vectors to generate high-resolutio...

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Abstract

The invention discloses a high-resolution dictionary based sparse representation image super-resolution reconstruction method. The method comprises the following steps of: (1) constructing a high-resolution brightness image library; (2) generating a sample training set; (3) learning an over-complete dictionary; (4) primarily establishing a high-resolution image brightness space; (5) establishing an image sample test set; (6) updating the high-resolution image brightness space; (7) calculating a weight sparse matrix; (8) reupdating the high-resolution image brightness space; (9) judging whether to repeat execution; and (10) outputting a high-resolution image. The high-resolution over-complete dictionary learned by the invention can be applied to different amplification factors. Sparse representation, non-local prior and data fidelity constraint are fully utilized, so that local information and global information can be comprehensively utilized. The method has higher super-resolution capacity; and the reconstructed image is closer to an actual image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a super-resolution reconstruction method of a single-frame color image based on machine learning and sparse representation in the fields of medical diagnosis, video monitoring, and HDTV imaging. Background technique [0002] In the fields of medical diagnosis, video surveillance, and high-definition television HDTV imaging, a single-frame image super-resolution reconstruction method that reconstructs a high-resolution image from a low-resolution image is used to improve image resolution. The current single-frame image super-resolution reconstruction technology is mainly based on low-resolution-high-resolution image block pairs to learn a data pair to achieve single-frame image super-resolution reconstruction. [0003] This kind of single-frame super-resolution reconstruction technology based on low-resolution-high-resolution data is also called super-resolution rec...

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Application Information

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IPC IPC(8): G06T5/50G06K9/66
Inventor 高新波沐广武张凯兵李洁邓成王斌王颖王秀美田春娜庾吉飞
Owner XIDIAN UNIV
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