An image blind super-resolution method and system

A technology for super-resolution and low-resolution images, applied in the field of image blind super-resolution methods and systems, can solve problems such as poor estimation accuracy, long running time, and complex calculations, and reduce difficulty and running time, convolution Ease of operation and improved estimation accuracy

Active Publication Date: 2022-05-27
XIAMEN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Traditional machine learning methods usually require a large number of optimization iterations, complex calculations, long running time, and poor estimation accuracy
In the method based on deep learning, the classic fuzzy kernel estimation method KernelGAN also has the problem of complex operation and long running time.

Method used

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  • An image blind super-resolution method and system
  • An image blind super-resolution method and system
  • An image blind super-resolution method and system

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Experimental program
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Embodiment 1

[0043] see figure 1 , this embodiment provides an image blind super-resolution method, the method includes the following steps:

[0044] Step 1: Obtain the trained blur kernel generation network and the spectrogram of the low-resolution image.

[0045] Step 2: Input the spectrogram into the trained blur kernel generation network to obtain a blur kernel corresponding to the low-resolution image.

[0046] Step 3: Determine the degradation feature map corresponding to the low-resolution image according to the blur kernel corresponding to the low-resolution image.

[0047] Step 4: splicing the low-resolution image and its corresponding degraded feature map to obtain a spliced ​​map.

[0048] Step 5: Input the mosaic image into a trained convolutional neural network to obtain a high-resolution image.

[0049] In an example, before step 1, a step of determining the frequency domain map of the low-resolution image may also be included, specifically, Fourier transform may be used t...

Embodiment 2

[0086] see Figure 5 , this embodiment provides an image blind super-resolution system, the system includes:

[0087] an acquisition module 501, configured to acquire the trained fuzzy kernel generation network and the spectrogram of the low-resolution image;

[0088] A blur kernel determination module 502, configured to input the spectrogram into the trained blur kernel generation network to obtain a blur kernel corresponding to the low-resolution image;

[0089] A degradation feature map determining module 503, configured to determine a degradation feature map corresponding to the low-resolution image according to the blur kernel corresponding to the low-resolution image;

[0090] The image stitching module 504 is used for stitching the low-resolution image and its corresponding degradation feature map to obtain a stitching map;

[0091] The high-resolution image determination module 505 is configured to input the mosaic image into the trained dense convolutional neural ne...

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Abstract

The invention discloses an image blind super-resolution method and system. The method includes: obtaining a trained fuzzy kernel generation network and a spectrogram of a low-resolution image; inputting the spectrogram into the trained fuzzy kernel generation network to obtain a blur kernel corresponding to the low-resolution image; The blur kernel corresponding to the low-resolution image determines the degradation feature map corresponding to the low-resolution image; splicing the low-resolution image and its corresponding degradation feature map to obtain a mosaic image; inputting the mosaic image through Trained convolutional neural network to obtain high-resolution images. The invention has the advantages of simple and fast operation and high accuracy.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a blind image super-resolution method and system. Background technique [0002] Image super-resolution (Super-Resolution, SR) technology refers to given one or more low-resolution (Low-Resolution, LR) images, using a specific algorithm to reconstruct it into an accurate high-resolution (High-resolution) image. -Resolution, HR) image technology. Image super-resolution technology is widely used in surveillance imaging, remote sensing imaging, medical imaging and other fields, and is also used as a preprocessing algorithm for various computer vision tasks. Most image super-resolution methods assume that the HR image is obtained from the LR image by a fixed degradation method (such as bicubic interpolation downsampling). To deal with a variety of degradation situations, the application of such methods has very large limitations. The degradation processes of LR images in the real wo...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T3/40G06N3/04G06N3/08
CPCG06T3/4038G06T3/4053G06N3/08G06T2200/32G06N3/045
Inventor 邱明吴国丽许全星黄世雄
Owner XIAMEN UNIV
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