吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (10): 2399-2404.doi: 10.13229/j.cnki.jdxbgxb20210561

• 计算机科学与技术 • 上一篇    

基于卷积神经网络的数字图像模糊增强算法

郭志军(),刘帅()   

  1. 湖南师范大学 信息科学与工程学院,长沙 410081
  • 收稿日期:2021-06-21 出版日期:2022-10-01 发布日期:2022-11-11
  • 通讯作者: 刘帅 E-mail:sdf1354945@126.com;lxjj201@126.com
  • 作者简介:郭志军(1982-),男,讲师,博士.研究方向:人工智能,无线通信,图像处理.E-mail:sdf1354945@126.com
  • 基金资助:
    湖南省自然科学基金项目(2020JJ4434);湖南省教育厅科研重点项目(19A312)

Digital image fuzzy enhancement algorithm based on convolutional neural network

Zhi-jun GUO(),Shuai LIU()   

  1. College of Information Science and Engineering,Hunan Normal University,Changsha 410081,China
  • Received:2021-06-21 Online:2022-10-01 Published:2022-11-11
  • Contact: Shuai LIU E-mail:sdf1354945@126.com;lxjj201@126.com

摘要:

为了解决数字图像灰度信息丢失、边缘模糊导致的图像效果不佳等问题,提出基于卷积神经网络的数字图像模糊增强算法。以图像灰度级作为论域构建特征模糊集合,拟定模糊隶属度,基于模糊熵计算图像边缘灰度值,同时,构建不同光照环境下限制对比度均衡派生图、伽马转换派生图、对数转换派生图、两通道增强派生图,借此微小调整图像像素方差,强化细节信息的表达,增强隶属度;最后,通过卷积神经网络筛选图像内渡越点,以此调整图像内对比度、平均亮度与像素方差,实现数字图像的模糊增强。实验结果表明,采用本文方法增强数字图像模糊的效果较好、且不易受环境影响。

关键词: 卷积神经网络, 数字图像, 模糊增强, 模糊隶属度, 对比度, 渡越点

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

中图分类号: 

  • TP92

图1

实验图像"

图2

样本数字图像模糊增强效果分析"

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