A new heterogeneous neural network model and its application in image enhancement

被引：17

作者：

Qi, Yunliang ^{[1
]}

Yang, Zhen ^{[1
]}

Lian, Jing ^{[2
]}

Guo, Yanan ^{[1
]}

Sun, Wenhao ^{[2
]}

Liu, Jizhao ^{[1
]}

Wang, Runze ^{[1
]}

Ma, Yide ^{[1
]}

机构：

[1] Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China

[2] Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, Lanzhou 730070, Gansu, Peoples R China

来源：

NEUROCOMPUTING | 2021年 / 440卷

基金：

中国国家自然科学基金;

关键词：

Visual cortex; Heterogeneous neural network; Receptive field; Image enhancement;

D O I：

10.1016/j.neucom.2021.01.133

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Based on visual cortical theory of Rybak, a new heterogeneous Rybak neural network (HRYNN) model is proposed for image enhancement. HRYNN is constructed with several Rybak neural network (RYNN) models proposed, which have different parameters corresponding to different neurons. We show that HRYNN can better represent prior information for edge detail enhancement than the logarithmic domain. To capture different resolution texture features of image, a novel receptive field model is proposed to solve the problem of detail enhancement. HRYNN model has excellent enhancement effect on the edge details of image based on the receptive field's lateral inhibitory characteristics. Moreover, the experimen-tal enhancement results of the colour images from Berkeley image Dataset show the validity and effi-ciency of the proposed enhancement method. Finally, three evaluation indicators are employed to measure the enhancement result. (c) 2021 Elsevier B.V. All rights reserved.

引用

页码：336 / 350

页数：15

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