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Super-resolution sparse representation method

A sparse reconstruction and super-resolution technology, applied in the field of image processing

Active Publication Date: 2012-10-10
BEIJING UNIV OF TECH
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Problems solved by technology

It can be seen that this method requires the help of a large number of external image libraries with high-resolution images.

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Embodiment Construction

[0032] Such as figure 1 As shown, this super-resolution sparse reconstruction method includes the following steps:

[0033] (1) Carry out space transformation on a given low-resolution color image to obtain its YCbCr space image, where Y is the nonlinear brightness component, Cb is the blue color difference component, Cr is the red color difference component, and the interpolation method is used for the Cb and Cr components carry out reconstruction;

[0034] (2) Construct a database for training, that is, a high-resolution image block X h and the low-resolution image block X l , and combined into database X;

[0035] (3) Use the sparse coding Sparse Coding method to generate a dictionary D for the database X, and decompose it into a dictionary D of high-resolution images h and a dictionary D of low-resolution images l ;

[0036] (4) Use D l The feature image corresponding to the image that is 2 times upsampled from the low-resolution image is used to solve the sparse co...

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Abstract

Under the premise of no extraneous high-resolution image library, a super-resolution sparse representation method for acquiring a high-resolution image is provided. The method comprises the steps of: (1) carrying out space conversion on a given low-resolution color image to obtain the YCbCr space image of the color image, and reconstructing the constituents of Cb and Cr by using an interpolation method; (2) constructing a database used for training, namely, a high-resolution image block Xh and a low-resolution image block Xl, and combining the two image blocks into a database X; (3) generating a dictionary D from the database X by using a sparse coding method, decomposing the dictionary D into a high-resolution image dictionary Dh and a low-resolution image dictionary Dl; (4) solving a sparse coefficient by using the Dl and characteristic images corresponding to an image of upsampling the low-resolution image by 2 times; (5) solving an image of upsampling the original low-resolution image by 3 times through the sparse coefficient and the Dh; and (6) combining Y, Cb, and Cr to obtain a YCbCr image, and converting the YCbCr image into an RGB image and storing the RGB image to obtain the final super-resolution representation image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a super-resolution sparse reconstruction method. Background technique [0002] As an important form of information for humans to perceive the world, images are rich and detailed in their content, which directly determines the level of detail humans perceive in the content. The higher the pixel density on the image unit scale, the clearer the image, the stronger the ability to express details, and the richer the information perceived by humans. This is what we call a high-resolution image. Therefore, in some specific fields, such as remote sensing images, satellite imaging fields, medical image fields, and some high-definition display fields, obtaining high-resolution digital images is a problem that people must consider. [0003] The method to improve the resolution of the image mainly includes technological improvement of the sensor manufacturing process to...

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Application Information

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IPC IPC(8): G06T5/00G06K9/62
Inventor 施云惠齐娜荆国栋丁文鹏
Owner BEIJING UNIV OF TECH
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