Improved transfer learning two-branch convolutional neural network image dehazing
Year: 2024
Type: article
Abstract: In view of the existing image dehazing algorithms, there are problems of incomplete dehazing and image color distortion. A dehazing network combining Transfer learning sub-net and Residual attention sub-net is proposed. First, the pretrained model of the transfer learning subnet is adopted to enhance the feature attributes of the samples. Second, the struct ure of the dual-branch network is constructed, and the residual attention sub-network is used to assist the transfer learning subnetwork to train the parameters of the network model. Finally, the method of tail ensemble learning is used to fuse the features of the dual network to obtain the model parameters of the dehazed image, so as to complete the image restoration task. The experimental results show that the algorithm proposed in the paper improves the PSNR index by 1.87 dB and 4.22 dB on the RESIDE data set and the O-HAZE data set respectively, and the SSIM index on the O-HAZE data set by 6.7%.
Authors: Yunhong Li, Moray Kidd, George Harris, Xueping Su, Jiaojiao Su, Ziming Gao
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4403385039