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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  • Summary
  • 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
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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 comprising the following steps: processing a first resolution image to form a second resolution image; determining a resolution dictionary according to the first resolution image and the second resolution image; segmenting an image to be reconstructed into multiple image blocks to be reconstructed; setting an amount-of-information threshold, and judging whether the amount of information of the image blocks to be reconstructed is greater than the amount-of-information threshold; and if the amount of information of the image blocks to be reconstructed is greater than the amount-of-information threshold, reconstructing the image blocks to be reconstructed according to the resolution dictionary to form first sub areas; and splicing the first sub areas to get a super-resolution reconstructed image. Real-time resolution reconstruction of an image is realized through a super-resolution reconstruction algorithm based on learning, and there is no need to change the hardware structure of an imaging system. The method has the advantages of low cost and high economic benefit.

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
CPCG06T3/4038G06T5/003G06T2207/20081
Inventor 邵晓鹏宫睿蔡祎霖王怡李轩
Owner XIDIAN UNIV
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