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An image deblurring algorithm based on Bi-Skip-Net

A deblurring and image technology, applied in image enhancement, image data processing, computing, etc., to achieve the effect of accurate texture restoration, low time complexity, and avoid time loss

Inactive Publication Date: 2019-03-01
PEKING UNIV SHENZHEN GRADUATE SCHOOL
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AI Technical Summary

Problems solved by technology

[0008] The present invention proposes a Bi-Skip-Net network as the generation network of GAN (Generative Adversarial Network), aiming to solve the shortcomings of the existing deep learning defuzzification algorithm

Method used

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  • An image deblurring algorithm based on Bi-Skip-Net
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  • An image deblurring algorithm based on Bi-Skip-Net

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Embodiment Construction

[0040] figure 1 Generative confrontation network mechanism adopted for the present invention. Among them, the blurred image is obtained by the generator to obtain the restored image, and the task of discrimination is to distinguish the restored image from the clear image as much as possible; and the task of the generator is to deceive the discriminator as much as possible to reduce the ability to distinguish between the two images.

[0041] The concrete steps of the embodiment of the present invention are as follows:

[0042] (1) Design generator and discriminator, the principle is as follows Figure 4 As shown, the blurred image of the building is passed through the Bi-Skip-Net generator to obtain a clear picture of the building; any other blurred image can be used to generate a clear picture with this model.

[0043] (2) Use the following loss function to train the network,

[0044]

[0045] in For the adversarial loss function, is the conditional loss function, an...

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Abstract

The invention relates to the field of digital image processing, in particular to an image deblurring method based on Bi-Skip-Net, which is a Bi-Skip-Net network for realizing fuzzy image restoration,and aims to solve the problems that the existing deep learning deblurring algorithm is high in time complexity and inaccurate in texture restoration, and a restored image has a grid effect. The Bi-Skip-Net network disclosed by the present invention is used as a generation network of a GAN (Generative Adversarial Network, in order to overcome the defects of an existing deep learning deblurring algorithm, through comparing with existing optimal algorithms, the time complexity of the method is improved by 0.1 s, and the original performance of an image complex image is improved by 1 dB on average.

Description

technical field [0001] The invention relates to the field of digital image processing, in particular to an image deblurring method based on Bi-Skip-Net, which realizes restoration of blurred images through a Bi-Skip-Net network. technical background [0002] Deblurring techniques are a widely studied topic in image and video processing. The blur caused by camera shake seriously affects the imaging quality and visual perception of the image in a certain sense. As an important branch of image preprocessing, the improvement of deblurring technology directly affects the performance of other computer vision algorithms, such as foreground segmentation, object detection, behavior analysis, etc.; at the same time, it also affects the encoding performance of images. Therefore, it is imperative to study a high-performance deblurring algorithm. [0003] Literature 1-3 introduces image and video processing deblurring technology, deep learning deblurring algorithm; Literature 1: Kupyn ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06N3/04
CPCG06N3/045G06T5/73
Inventor 李革张毅伟王荣刚王文敏高文
Owner PEKING UNIV SHENZHEN GRADUATE SCHOOL
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