改进迁移学习的双分支卷积神经网络图像去雾

Improved transfer learning based dual-branch convolutional neural network image dehazing

针对现有图像去雾算法存在去雾不彻底和图像颜色失真的问题,提出一种迁移学习子网络和残差注意力子网络相结合的图像去雾模型。采用迁移学习子网络的预训练模型增强样本的特征属性;构建双分支网络结构,并利用残差注意力子网络辅助迁移学习子网络训练网络模型的参数;利用尾部集成学习的方法融合双网络的特征,得到去雾图像的模型参数,完成图像恢复任务。实验结果表明:所提算法在RESIDE数据集和O-HAZE数据集上PSNR指标比GCANet分别提高了1.87 dB和4.22 dB,在O-HAZE数据集上SSIM指标比GCANet提高了6.7%。

To address the problems of incomplete dehazing and image color distortion in the existing image dehazing algorithms,a dehazing network combining transfer learning sub-net and residual attention sub-net is proposed.First,the pre-trained model of the transfer learning subnet is adopted to enhance the feature attributes of the samples.Second,the structure of the dual-branch network is constructed,and the residual attention sub-network is used to assist the transfer learning sub-network 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 dataset and the O-HAZE dataset respectively compared to GCANet,and the SSIM index on the O-HAZE dataset by 6.7%compared to GCANet.