T=题名(书名、题名),A=作者(责任者),K=主题词,P=出版物名称,PU=出版社名称,O=机构(作者单位、学位授予单位、专利申请人),L=中图分类号,C=学科分类号,U=全部字段,Y=年(出版发行年、学位年度、标准发布年)
AND代表“并且”;OR代表“或者”;NOT代表“不包含”;(注意必须大写,运算符两边需空一格)
范例一:(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
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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.
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