Online dictionary learning super-resolution reconstruction method based on sparse representation

A super-resolution reconstruction and dictionary learning technology, which is applied in image analysis, character and pattern recognition, image data processing, etc., can solve problems such as visual artifacts, limited ability to retain detailed information, and poor reconstruction effect

Pending Publication Date: 2019-04-16
CHINA UNIV OF MINING & TECH
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Problems solved by technology

At present, the traditional super-resolution reconstruction method has poor reconstruction effect,

Method used

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  • Online dictionary learning super-resolution reconstruction method based on sparse representation
  • Online dictionary learning super-resolution reconstruction method based on sparse representation
  • Online dictionary learning super-resolution reconstruction method based on sparse representation

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[0060] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0061] The embodiment of the present invention provides a method for online dictionary learning super-resolution reconstruction based on sparse representation, referring to figure 2 shown, including:

[0062] 1) Based on the traditional sparse prior of the image, AR and non-local self-similarity are added as additional supplementary information of the image, and a non-local regularized super-resolution reconstruction model of the ...

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Abstract

The invention relates to an online dictionary learning super-resolution reconstruction method based on sparse representation. The online dictionary learning super-resolution reconstruction method comprises the three parts of prior information, dictionary learning and sparse reconstruction. The dictionary training part adopts online dictionary learning, so that not only is external image library information effectively utilized, but also the image information is added to update the dictionary. Besides, on the basis of sparse prior, a local autoregression model and non-local self-similarity areadded at the same time to serve as prior information, a non-local regularized super-resolution reconstruction model is established, and various structural features of the image are reconstructed. In the sparse reconstruction stage, a sparse coefficient is determined by using multi-scale self-similarity sparse representation, a non-local constraint item is constructed according to a corresponding relation between different scale similar blocks, and additional information of a multi-scale self-similarity structure is introduced into a reconstruction process in an image reconstruction model; Themethod not only can reduce the dependence of the test image on the training image set, but also can overcome the local distortion or fuzziness of the image block in the reconstruction process, therebyfurther improving the quality of the reconstructed image.

Description

technical field [0001] The invention relates to the technical field of image signal processing, in particular to a method for super-resolution reconstruction based on online dictionary learning with sparse representation. Background technique [0002] There are many ways for human beings to understand the world and obtain information, including auditory, tactile and olfactory, and visual information is also indispensable. The importance of visual information is often the most important, far surpassing other ways of acquisition. Therefore, the image quality obtained through visual information will directly affect our cognition and judgment of things. [0003] High-resolution images have higher pixel density and contain more detailed information, which is of great significance and help to various special fields such as image feature extraction, target recognition and positioning, satellite remote sensing imaging, and medical imaging. However, in practice, the obtained image ...

Claims

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Application Information

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IPC IPC(8): G06T3/40G06K9/62
CPCG06T3/4023G06T3/4076G06T2207/20081G06F18/2136G06F18/28
Inventor 程德强于文洁李腾腾白帅刘钊李晓晖
Owner CHINA UNIV OF MINING & TECH
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