Image blind motion blur removing method based on cyclic multi-scale generative adversarial network

A motion blur and multi-scale technology, applied in the field of image processing, can solve the problems affecting the restoration effect and the accuracy of blur kernel estimation, and achieve the effect of easy training, omitting the blur kernel estimation process, and good restoration effect

Active Publication Date: 2019-10-25
HANGZHOU DIANZI UNIV
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Problems solved by technology

In the blind deblurring of a single moving image, the blur kernel and its size of the blurred image are unkno

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  • Image blind motion blur removing method based on cyclic multi-scale generative adversarial network
  • Image blind motion blur removing method based on cyclic multi-scale generative adversarial network
  • Image blind motion blur removing method based on cyclic multi-scale generative adversarial network

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[0037] The specific implementation of the present invention will be further described below.

[0038] The fuzzy image set B is input into the generator G, and the generator output image set L is obtained, which is used as the input of the discriminator D, and the discriminator's discrimination result is obtained. In the same way, the clear image set S is also used as the input of the discriminator to obtain the discrimination result. The determination result indicates whether the input is from a clear image set or a generated image set. If the determination result is greater than 0.5, it is determined as the clear image set S; otherwise, it is determined as the generator output image set L. Calculate the error between the judgment result and the real label data, use the gradient descent algorithm to optimize the discriminator, then calculate the error average of the generated image and the clear image, and use the gradient descent algorithm to optimize the generator. Alternately...

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Abstract

The invention discloses an image blind motion blur removing method based on a cyclic multi-scale generative adversarial network. According to the method, a cyclic multi-scale encoder and a decoder areused as generators, and a corresponding decision device is constructed. And the adversarial loss, the multi-scale mean square error and the multi-scale gradient error of the generated image and the clear image are taken as a loss function of the generative adversarial network, and the loss function is optimized by a gradient descent method. According to the method, the generative adversarial network is used to learn the relationship between the motion blurred image and the corresponding clear image, and a complex blurred kernel estimation process is omitted. According to the method, the edgefeatures of the image can be extracted, a simpler network structure and fewer parameters are achieved, the network model is easier to train, and the restoration effect is good.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image blind motion blur removal method based on a circular multi-scale generation confrontation network. Background technique [0002] Because it is difficult to maintain a relatively static state between the shooting device and the imaging object, it will cause motion blur in the image. However, in daily life, traffic safety, medicine, military investigation and other fields, it is particularly important to be able to obtain a clear image. [0003] The blurring of moving images can be regarded as the formation of additive noise pollution after the clear image is convolved with a two-dimensional linear function. This linear function is called the point spread function or convolution kernel, and it contains the blur information of the image. Blind deblurring of an image refers to restoring the original clear image only by relying on the information of the blurred image...

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Application Information

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IPC IPC(8): G06T5/00G06T7/13G06K9/62G06N3/04G06N20/00
CPCG06T5/003G06N20/00G06T7/13G06N3/045G06F18/214
Inventor 陈华华陈富成叶学义
Owner HANGZHOU DIANZI UNIV
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