基于卷积神经网络的数字图像模糊增强算法

doi:10.13229/j.cnki.jdxbgxb20210561

Abstract

Abstract:

In order to solve the problems of gray information loss and poor image effect caused by edge blur of digital image， a digital image fuzzy enhancement algorithm based on convolution neural network is proposed. At the same time， restricted contrast balanced derivative image， gamma conversion derivative image， logarithmic conversion derivative image and two-channel enhancement derivative image are constructed under different lighting environments， so as to slightly adjust the variance of image pixels and enhance the expression of detail information. Finally， the convolution neural network is used to filter the transition points in the image， so as to adjust the contrast， average brightness and pixel variance in the image， and the fuzzy enhancement of the digital image was realized. Experimental results show that the proposed method can enhance the effect of digital image blur better， and is not easy to be affected by the environment.

Key words: convolutional neural network, digital image, fuzzy enhancement, fuzzy membership degree, contrast, transit point

CLC Number:

TP92

Zhi-jun GUO,Shuai LIU. Digital image fuzzy enhancement algorithm based on convolutional neural network[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(10): 2399-2404.

Figures/Tables 2

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References 17

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Digital image fuzzy enhancement algorithm based on convolutional neural network

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