Improved AOD-Net Algorithm for Dehazing Road Traffic Images
In order to address the problem that existing image dehazing algorithms cannot simultaneously consider both dehazing effects and real-time performance when processing road traffic images,a fast all-in-one dehazing network(AOD-Net)algorithm is improved in this study.Firstly,SE channel attention is added to the AOD-Net to adaptively allocate channel weights and focus on important features.Secondly,a pyramid pooling module is introduced to enlarge the receptive field of the network and fuse the features in different scales,so as to better capture image information.Finally,a composite loss function is used to simultaneously focus on image pixel information and structural texture information.Experimental results show that the improved AOD-Net algorithm increases the peak signal-to-noise ratio(SNR)of road traffic images by 2.52 dB after dehazing,and the structural similarity reaches 91.2％.The algorithm complexity and dehazing time are slightly increased,but still meet real-time requirements.
NETL
NSTL
万方数据
维普
