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Image super-resolution reconstruction processing method

A processing method and super-resolution technology, applied in the field of image processing, can solve problems such as lack of adaptability to local image structures, affecting sparse coding efficiency, etc.

Inactive Publication Date: 2017-08-18
NANJING INST OF TECH
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Because it lacks the adaptability to the local structure of the image, that is, it cannot effectively represent all the changing structures in the image, and many of its atoms are irrelevant to a specific image block, which will affect the sparse coding efficiency.

Method used

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Embodiment Construction

[0071] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0072] First, the sparse representation reconstruction and non-local self-similar priors are elaborated:

[0073] sparse representation reconstruction

[0074] A single image SRR is to reconstruct a high-resolution image x when a single low-resolution image y is known, which can be expressed as:

[0075] y=DHx+n (1)

[0076] That is, y is the result of x being processed by fuzzy operator H and downsampling matrix D and superimposed noise n.

[0077] SRR is an ill-conditioned inverse problem. Th...

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Abstract

The invention discloses an image super-resolution reconstruction processing method. On the basis of adaptive sparse representation reconstruction research, a series of corresponding sub-dictionaries are obtained through image block subset learning, and then an optimal sub-dictionary is adaptively selected for each reconstructed image block, so that more accurate sparse representation modeling can be carried out and the algorithm effect and efficiency can be improved. In order to improve the capability of a sparse representation model, a nonlocal self-similarity prior item is introduced, a nonlocal self-similarity model is improved by utilizing a bilateral filtering thought, and spatial position distance constraints among pixels are introduced, so that image edge information is better kept. Moreover, distance measurement of nonlocal self-similarity is improved, so that the calculation amount is reduced. An experiment proves that the noise influence can be effectively suppressed and image edge details can be kept, and the method has a certain superiority in the aspects of peak signal-to-noise ratios and visual effects.

Description

technical field [0001] The invention relates to an image super-resolution reconstruction processing method, which belongs to the technical field of image processing. Background technique [0002] Image Super-resolution Reconstruction (SRR) refers to the use of one or more low-resolution (Low-resolution, LR) images, combined with a certain prior to reconstruct a high-resolution ( High-resolution, HR) image process. It can improve the spatial resolution of images by using signal processing related technologies without changing the existing imaging system, which is conducive to the subsequent application of images in many fields such as medicine, remote sensing, military monitoring, and image compression. [0003] The basic concepts and methods of SRR were proposed by Harris and Goodman in the 1960s. Tsai and Huang [8] first proposed a multi-image SRR algorithm based on frequency domain approximation in 1984. Since then, SRR technology has entered a rapid progression stage. ...

Claims

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Application Information

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IPC IPC(8): G06T3/40
CPCG06T3/4053
Inventor 徐梦溪黄陈蓉杨芸施建强
Owner NANJING INST OF TECH
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