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Image super resolution reconstruction method and device based on dictionary matching

A technology of super-resolution reconstruction and dictionary matching, which is applied in image data processing, graphic image conversion, instruments, etc., to achieve the effect of improving quality, high matching degree and high precision

Active Publication Date: 2016-03-09
PEKING UNIV SHENZHEN GRADUATE SCHOOL
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

This patented technology provides an algorithm that uses dictionaries (a set of rules) to search through images based on their content or structure. It then finds matches between these patterns found within each frame and adds them back together creating a more accurate version of what was captured earlier. These techniques help enhance the resolution and clarity of digital photography videos while improving overall performance.

Problems solved by technology

This patented technical solution describes two types of techniques for increasingly precise and accurate edge detection in low-quality images: 1 ) interpolating or shading algorithms that use discrete values from previously generated pixels; 2) training dictionaries that can accurately match lower-density versions of these pixel values without sacrificing their sharpness.

Method used

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  • Image super resolution reconstruction method and device based on dictionary matching
  • Image super resolution reconstruction method and device based on dictionary matching
  • Image super resolution reconstruction method and device based on dictionary matching

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Embodiment 1

[0019] Please refer to figure 1 , figure 1 This is a flowchart of the method in Embodiment 1 of this application. Such as figure 1 As shown, an image super-resolution reconstruction method based on dictionary matching may include the following steps:

[0020] 101. Establish a matching dictionary library.

[0021] 102. Input the image block to be reconstructed into the multilayer line filter network, and extract the local features of the image block to be reconstructed.

[0022] Specifically, step 102 may include: Step 1: The multilayer line filter network includes a filter layer, and the first-stage filter of the filter layer uses N line filter windows of different sizes to filter the input image block to be reconstructed to obtain the corresponding N filtered images of, and output to the next-stage filter, the filtered image includes: line features of the image, where N is an integer greater than 1.

[0023] Step 2: The second-stage filter of the filter layer uses M line filter wind...

Embodiment 2

[0068] Such as Figure 5 As shown, the embodiment of the present application provides an image super-resolution reconstruction device based on dictionary matching, including: a establishing unit 30 for establishing a matching dictionary library, and further including:

[0069] The extracting unit 31 is configured to input the image block to be reconstructed into the multilayer line filter network, and extract the local features of the image block to be reconstructed.

[0070] The matching unit 32 is configured to find the local feature of the low-resolution image block with the highest similarity to the local feature of the image block to be reconstructed from the matching dictionary library.

[0071] The searching unit 33 searches for the residual value of the joint sample of the local feature of the low-resolution image block with the highest similarity in the matching dictionary library.

[0072] The difference amplifying unit 34 is configured to interpolate and amplify the local fe...

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Abstract

The invention provides an image super resolution reconstruction method and a device based on dictionary matching. A matching dictionary is built, a to-be-reconstructed image is inputted to a multilayer line filter network, local features of the to-be-reconstructed image are extracted, local features of a low-resolution image block with the highest similarities with the local features of the to-be-reconstructed image are searched from the matching dictionary, residual of a joint sample for the local features of the low-resolution image block with the highest similarities is searched in the matching dictionary, interpolation amplification is carried out on the local features of the low-resolution image block with the highest similarities, and with the addition of the residual, a high-resolution image block after reconstruction is acquired. As the local features of the to-be-reconstructed image are extracted via the multilayer line filter network, the precision is higher, the matching degree is higher when matching with the matching dictionary is carried out subsequently, and the reconstructed image has a good quality. Thus, the quality of the reconstructed high-resolution image can be greatly enhanced.

Description

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Claims

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Application Information

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Owner PEKING UNIV SHENZHEN GRADUATE SCHOOL
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