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A real-time super-resolution reconstruction method

A super-resolution reconstruction and resolution technology, applied in the field of image and video, can solve the problems of inability to realize real-time super-resolution reconstruction and low reconstruction efficiency, and achieve the effect of reducing the number of dictionary atoms, low cost, and improving computing efficiency.

Active Publication Date: 2020-05-12
XIDIAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although the super-resolution reconstruction algorithm based on learning is effective, but due to the need to train a large amount of prior information, the reconstruction efficiency is relatively low, and the purpose of real-time super-resolution reconstruction cannot be achieved.

Method used

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  • A real-time super-resolution reconstruction method
  • A real-time super-resolution reconstruction method
  • A real-time super-resolution reconstruction method

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0049] See figure 1 , figure 1 It is a schematic diagram of a real-time super-resolution reconstruction method provided by an embodiment of the present invention. The method includes the following steps:

[0050] Step 1. Form a second resolution image according to the first resolution image processing, and determine a resolution dictionary from the first resolution image and the second resolution image;

[0051] Step 2: Perform block processing on the image to be reconstructed to form multiple image blocks to be reconstructed;

[0052] Step 3. Set the information volume threshold and determine whether the information volume of the image block to be reconstructed is greater than the information volume threshold; if so, perform image reconstruction on the image block to be reconstructed according to the resolution dictionary to form a first sub area;

[0053] Step 4. Perform image stitching on the first sub-region to obtain a super-resolution reconstructed image.

[0054] Among them, fo...

Embodiment 2

[0090] This embodiment provides a detailed description of the technical solution of the present invention on the basis of the foregoing embodiment. Specifically, the method includes:

[0091] Step 1: Using a large number of samples of high-resolution images (ie, the first resolution image), the high-resolution images are subjected to blur processing and N-fold down-sampling according to the modified degradation model to obtain the corresponding low-resolution images (ie, the first resolution image). Two-resolution image) sample.

[0092] Step 2: For the low-resolution image obtained in step 1, the image features are extracted through the feature extraction algorithm to obtain the high-resolution feature information X of the space target s (I.e. first resolution feature information) and low resolution feature information Y s (Ie the second resolution feature information).

[0093] Step 3: Use the K-SVD algorithm to jointly train the feature information to obtain a high-resolution dic...

Embodiment approach

[0110] S1: Perform block segmentation on a large number of low-resolution image samples to obtain image blocks;

[0111] S2: Use an edge extraction algorithm to extract edge information of low-resolution image blocks, and count the information volume of each low-resolution image block and the distribution of information volume of all low-resolution image blocks;

[0112] S3: Select the value with the highest amount of information in the low-resolution image block, obtain the pixel value of the low-resolution image block as F1, f=F1 / 4, then f*40% <= threshold <=f*60%, take several representative thresholds in this range, for example, you can take the following representative thresholds: f*40%, f*45%, f*50%, f*55%, f*60%, calculate each The learning-based sparse representation of each threshold point corresponds to the reconstruction time of the image super-resolution reconstruction algorithm and the resolution of the reconstructed image, which can be judged according to subjective ev...

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Abstract

The invention relates to a real-time super-resolution reconstruction method, which forms a second resolution image according to the first resolution image processing, and determines the resolution dictionary from the first resolution image and the second resolution image; and divides the image to be reconstructed into blocks. Process and form multiple image blocks to be reconstructed; set an information amount threshold and determine whether the information amount of the image block to be reconstructed is greater than the information amount threshold; if so, image the image block to be reconstructed according to the resolution dictionary The first sub-region is reconstructed and image splicing is performed on the first sub-region to obtain a super-resolution reconstructed image. The present invention uses a learning-based super-resolution reconstruction algorithm to realize real-time resolution reconstruction of images without changing the hardware structure of the imaging system, and has the advantages of low cost and high economic benefits.

Description

Technical field [0001] The invention relates to the field of image and video, in particular to a real-time super-resolution reconstruction method. Background technique [0002] Image super-resolution reconstruction technology uses a group of low-quality, low-resolution images (or motion sequences) to generate a single high-quality, high-resolution image. Image super-resolution reconstruction has a wide application field, and there are important application prospects in military, medicine, public safety, computer vision, etc. In the field of computer vision, image super-resolution reconstruction technology has the potential to realize the conversion of images from detection level to recognition level, or even further to the identification level. Image super-resolution reconstruction technology can improve image recognition ability and recognition accuracy. The image super-resolution reconstruction technology can realize the focused analysis of the target, so that the higher spat...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00G06T3/40
CPCG06T3/4038G06T2207/20081G06T5/73
Inventor 邵晓鹏宫睿蔡祎霖王怡李轩
Owner XIDIAN UNIV
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