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A parallel method for image super-resolution and deblurring

A super-resolution and deblurring technology, which is applied in image analysis, image enhancement, image data processing, etc., can solve the problems of poor effect and loss of high-frequency details of the image, and achieve clear output, reduce the amount of calculation, and sharpen the feature map Effect

Active Publication Date: 2021-01-19
XI AN JIAOTONG UNIV
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Problems solved by technology

[0005] It is difficult for the existing image deblurring algorithm to estimate the blur kernel suitable for the whole image. On the other hand, the existing image super-resolution method will lose the high-frequency details of the image. When trying to combine these two tasks, the effect will be better. Difference

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  • A parallel method for image super-resolution and deblurring

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Embodiment approach

[0027] According to one embodiment of the present invention, it mainly includes the following steps:

[0028] Step 1: Image data set preprocessing, such as DIV 2K high-definition image data set, after randomly cropping image blocks on each training set image and performing random flipping, as the training true value of the super-resolution branch of the neural network, the cropped The obtained image block is interpolated and scaled as the training truth value of the deblurring branch of the neural network, and then the Gaussian kernel is artificially added to it for image blurring, and Gaussian noise at a level of 0.1 is added, and the final image is used as the input of the neural network.

[0029] Step 2: Construct a neural network, use a neural network encoding-decoding module based on deep learning to extract the features of the entire input image, and invent a two-branch structure to achieve image deblurring and super-resolution tasks respectively.

[0030] Among them, th...

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Abstract

The invention discloses a method for achieving image super-resolution and deblurring in parallel. After obtaining a suitable data set, the input image features are quickly extracted through a specially designed encoding-decoding neural network module with feature information bridging. , and the output feature map is simultaneously used as an image super-resolution branch and a deblurring branch for related task processing, which not only reduces the amount of calculations, but also, when training the network, the two branch networks proposed by the present invention can perform different tasks to varying degrees. The shared feature map is stimulated to improve the effects of the super-resolution branch and the deblurring branch.

Description

technical field [0001] The invention belongs to the field of computer vision and image processing, in particular to a method for realizing image super-resolution and deblurring in parallel. Background technique [0002] In recent years, with the development and maturity of deep learning technology, image super-resolution and image deblurring algorithm research has received more and more attention, and great progress has been made in the algorithm. [0003] The purpose of image super-resolution is to restore high-resolution images from low-resolution images. It can not only generate satisfactory high-resolution images, but also provide deeper image processing processes such as target detection and face recognition. Provides a higher quality image source. However, long-term exploration has found that camera shake, out-of-focus, turbulence and other phenomena seriously hinder the research of image super-resolution methods. [0004] Image deblurring is a method to restore a cl...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00
CPCG06T2207/20084G06T2207/20081G06T5/73
Inventor 王飞张康龙张昕昳韦昭谷宇祝捷董航
Owner XI AN JIAOTONG UNIV
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