基于深度卷积神经网络的自适应图像去雾算法

doi:10.16180/j.cnki.issn1007-7820.2020.08.012

Abstract

Abstract:

Due to the influence of air particles such as fog, haze, dust and so on, the image of outdoor photography was gray and white. However, the existing image de fogging algorithm had the problems of over dependence on prior information and inaccurate transmission calculation. In order to solve the above problems, an adaptive image defogging algorithm based on the depth convolution neural network was proposed in this paper. The algorithm realized the defogging of the foggy image based on the atmospheric scattering model. Three full convolution networks including shallow extraction, parallel extraction and deep fusion, were designed to realize the fusion of the shallow and deep features of the image, which greatly improved the accuracy of the transmittance image. The experimental results showed that the algorithm proposed in this paper had a good defogging effect on outdoor fog map, and the effect of defogging details was better.

Key words: deep convolution, neural network, image defogging, atmospheric scattering model, outdoor fog map, full convolution, transmission, detail dehazing

CLC Number:

TP391.41

HE Yihong,LI Yanfeng,HUANG Shukai,TAN Wanchuan. Adaptive Image Dehazing Algorithm Based on Deep Convolutional Neural Network[J].Electronic Science and Technology, 2020, 33(8): 70-73.

Figures/Tables 5

References 11

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Adaptive Image Dehazing Algorithm Based on Deep Convolutional Neural Network

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