Single Image Dehazing Based on Multiple Convolutional Neural Networks

被引：0

作者：

Chen Q.-J. ^{[1
]}

Zhang X. ^{[1
]}

机构：

[1] College of Science, Xi'an University of Architecture and Technology, Xi'an

来源：

Zhang, Xue (zhangxueyanice@163.com) | 1739年 / Science Press卷 / 47期

基金：

中国国家自然科学基金;

关键词：

Atmospheric scattering model; Convolution neural network; Image dehazing; Multi-scale convolution; Recursive bilateral filtering;

D O I：

10.16383/j.aas.c190156

中图分类号：

学科分类号：

摘要：

Aiming at the problem that the existing single image dehazing algorithm, a single image dehazing algorithm based on multiple convolutional neural networks is proposed. Firstly, the Y, U and V components transformed by YUV of foggy day RGB images were used to construct a multiple convolutional neural network to obtain haze characteristics adaptively. The network structure is composed of two subnetworks, the deeper one predicts the brightness channel of the clear image, and the lighter one predicts the chromaticity channel and saturation channel. Finally, recursive bilateral filtering is adopted to filter the image after dehazing to obtain a clearer fog-free image. The experimental results show that this algorithm has good contrast and clarity in both synthetic and natural foggy image data sets, and is superior to other comparison algorithms in terms of subjective and objective evaluation. Copyright © 2021 Acta Automatica Sinica. All rights reserved.

引用

页码：1739 / 1748

页数：9

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