Deeplearning method for single image dehazing based on HSI colour space

作     者：CHEN Yong TAO Meifeng GUO Hongguang 陈永;陶美风;郭红光

作者机构：School of Electronics and Information Engineering Lanzhou Jiaotong University Lanzhou 730070 China 

基　　金：National Natural Science Foundation of China(No.61963023) MOE(Ministry of Education in China)Project of Humanities and Social Sciences(No.19YJC760012) 

出 版 物：《Journal of Measurement Science and Instrumentation》 (测试科学与仪器（英文版）)

年 卷 期：2021年第12卷第4期

页      码：423-432页

摘      要：The traditional single image dehazing algorithm is susceptible to the prior knowledge of hazy image and colour distortion.A new method of deep learning multi-scale convolution neural network based on HSI colour space for single image dehazing is proposed in this paper,which directly learns the mapping relationship between hazy image and corresponding clear image in colour,saturation and brightness by the designed structure of deep learning network to achieve haze ***,the hazy image is transformed from RGB colour space to HSI colour ***,an end-to-end multi-scale full convolution neural network model is *** multi-scale extraction is realized by three different dehazing sub-networks:hue H,saturation S and intensity I,and the mapping relationship between hazy image and clear image is obtained by deep ***,the model was trained and tested with hazy data *** experimental results show that this method can achieve good dehazing effect for both synthetic hazy images and real hazy images,and is superior to other contrast algorithms in subjective and objective evaluations.

主 题 词：image processing image dehazing HSI colour space multi-scale convolution neural network 

学科分类：08[工学] 0812[工学-测绘类] 

核心收录：

D　O　I：10.3969/j.issn.1674-8042.2021.04.006

馆 藏 号：203106377...