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Sparse representation single-frame image super-resolution reconstruction algorithm based on main structure separation

A technology of super-resolution reconstruction and sparse representation, applied in image analysis, image enhancement, image generation, etc., can solve problems such as limited effects, achieve the effects of reducing running time, reducing computational complexity, and preventing overfitting

Active Publication Date: 2018-06-05
NANJING UNIV OF SCI & TECH
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Problems solved by technology

This method has very limited effect, s

Method used

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  • Sparse representation single-frame image super-resolution reconstruction algorithm based on main structure separation
  • Sparse representation single-frame image super-resolution reconstruction algorithm based on main structure separation
  • Sparse representation single-frame image super-resolution reconstruction algorithm based on main structure separation

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Example Embodiment

[0078] Example 1

[0079] Combine Image 6 , Figure 7 with Figure 8 , The three pictures "foreman", "comic" and "baby" are super-resolution reconstruction processing based on the sparse representation single-frame image super-resolution reconstruction algorithm based on the separation of the main structure, the magnification factor is 2 times, and the reconstructed image is relative to The details of the peak-to-noise ratio and running time of the original high-resolution image are compared with other new algorithms.

[0080] Table 1-1 Comparison of the signal-to-noise ratio (PSNR) and running time of the pictures "foreman", "comic" and "baby" after magnification 2 times

[0081]

[0082] Combine Figure 2-Figure 5 ,From figure 2 with image 3 It can be seen that the proposed adaptive dictionary size can reconstruct a better high-resolution image compared to other dictionary sizes, which is well adapted to the characteristics of different images.

[0083] From Figure 4 with Figur...

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Abstract

The invention discloses a sparse representation single-frame image super-resolution reconstruction algorithm based on main structure separation. According to the algorithm, first, an input image is decomposed through improved correlation total variation to obtain a main structure and texture of the image, and then the main structure and the texture are processed separately; for main structure components, a self-driven learning algorithm based on self-similarity is constructed to perform reconstruction; and for texture components, an external database is adopted to perform sparse representationreconstruction. Through the algorithm, the correlation total variation is introduced for the first time to solve the super-resolution problem, the main structure obtained through separation has sharpedges, strong self-similarity is provided, complicated calculation of a traditional method is avoided while the reconstruction effect is improved, and efficiency is improved; and the complexity of the texture part is lowered, various texture patterns can be reconstructed through one external dictionary, the problem that dictionary size is not enough for solving the problem of complicated patternchanges in a traditional dictionary learning super-resolution method is avoided, and the algorithm can cope with different types of images.

Description

technical field [0001] The invention relates to image super-resolution technology, in particular to a sparse representation single-frame image super-resolution reconstruction algorithm based on main structure separation. Background technique [0002] The development of the information technology era has made people have more and more channels for obtaining images. However, due to the limitations of the imaging system, including point spread function (PSF) and spectral aliasing effects, some image quality cannot meet people's expectations. Compared with spending a lot of money On imaging equipment, it saves time and effort to enhance image quality through some software algorithms. Image super-resolution technology is one of them, and it has been widely used in medical images, satellite imaging, target recognition and video surveillance and other fields. [0003] Early super-resolution algorithms were mainly based on multi-frame images, such as optical flow method, POCS, IBP, ...

Claims

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Application Information

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IPC IPC(8): G06T11/00G06T7/40G06T3/40G06K9/46G06K9/62
CPCG06T3/4053G06T11/003G06T2207/20081G06T2211/416G06V10/40G06V10/513G06F18/28
Inventor 隋修宝吴健高航陈钱顾国华刘源吴少迟吴骁斌匡晓东刘程威
Owner NANJING UNIV OF SCI & TECH
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