Mixed-resolution sparse dictionary learning-based image super-resolution method

A low-resolution image and sparse dictionary technology, applied in the field of digital images, can solve problems such as consuming large computing resources, achieve the effects of enriching image texture information, improving expressive ability, and sharpening image edges

Active Publication Date: 2018-07-27
WUHAN UNIV
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Problems solved by technology

Multi-component dictionaries can improve the expression accuracy of regions with different structural features, but pre-dividing images into regions with different attributes requires a lot of computing resources

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  • Mixed-resolution sparse dictionary learning-based image super-resolution method
  • Mixed-resolution sparse dictionary learning-based image super-resolution method
  • Mixed-resolution sparse dictionary learning-based image super-resolution method

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Embodiment Construction

[0028] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the following in conjunction with the attached figure 1 The present invention will be further described in detail with reference to the implementation examples and implementation examples. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0029] please see figure 1 , a kind of image super-resolution method based on mixed resolution sparse dictionary learning provided by the present invention comprises the following steps:

[0030] Step 1: Take the images in the image library as training samples, train two types of low-resolution dictionaries with resolutions of 3×3 and 5×5, and obtain two pairs of high- and low-resolution dictionaries; among them, 3×3 and 5×5 The resolutions of the high-resolution dictionaries corresponding ...

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Abstract

The invention discloses a mixed-resolution sparse dictionary learning-based image super-resolution method. The method comprises a dictionary training process and an image super-resolution reconstruction process. In the dictionary learning process, a dictionary is generated by performing random sampling on training sample images, and similar operation is repeated to obtain dictionaries with different resolutions. In the image super-resolution reconstruction process, the images are subjected to multi-resolution sparse expression based on the mixed-resolution dictionaries, specifically texture information richness in the images is judged through a variance, image blocks with rich texture information are subjected to super-resolution reconstruction by using the low-resolution dictionary, and image blocks with relatively non-rich texture information are reconstructed by using the high-resolution dictionary. The object edges in the images can be sharpened and the texture information of the images can be enhanced; and the smooth and blurring effects of the super-resolution enlarged images are reduced.

Description

technical field [0001] The invention belongs to the technical field of digital images and relates to an image super-resolution method, in particular to an image super-resolution method based on mixed resolution sparse dictionary learning. technical background [0002] The spatial resolution of images is an important factor affecting the performance of image processing tasks. There are many technical means to improve the resolution of images, and image super-resolution reconstruction is one of them. Super-resolution image reconstruction can be seen as the process of reconstructing a high-resolution image from a single or multiple low-resolution images. Image super-resolution technology has been widely used in video surveillance, video format conversion, medical digital imaging, satellite images and other fields. In these fields, how to restore the detail information in the image with loss of detail information becomes the key to image super-resolution reconstruction. [00...

Claims

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Application Information

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IPC IPC(8): G06T3/40G06K9/46
CPCG06T3/4076G06V10/40G06V10/464G06V10/513
Inventor 王中元全敦权韩镇肖晶
Owner WUHAN UNIV
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