关键词: 卷积神经网络, 深度学习, 特征融合, 图像去雨

Abstract: Rain streaks result in the occlusion of image content,which seriously affects the human visual effect and the processing performance of subsequent systems.Existing deep learning-based methods improve de-raining performance at the expense of complex network structure and parameter burden,which makes these methods difficult for serving practical applications.Therefore,a deep adjacently connected network structure was proposedin this paper.By focusing on the relationship between learned feature maps in depth networks,a fusion operation is designed to connect the adjacent features to obtain rich and more effective feature representation.Experiments on three public synthetic datasets and real-world rainy images show that the proposed method improves de-raining performance on both subjective and objective evaluations.The average structural similarity (SSIM) value on the synthetic dataset Rain100H is 0.84,and the average SSIM values on the synthetic dataset Rain100L and Rain1200 are 0.96 and 0.91.In real-world rainy images,the proposed method can effectively remove the foreground rain streaks while protecting background image information to obtain better visual quality.Compared with JORDER,the proposed method achieves comparable de-raining results,and can reduce the model parameters and CPU runtime by one and two orders of magnitude,respectively.Experimental data demonstrate that fusing adjacent features in the deep network can generate more effective representation.Therefore,although the proposed method contains relative few parameters and simple neural network structure,it can still achieve better ima-ge de-raining performance and solve the problems of parameter burden and complex network structure in existing methods.Mo-reover,the network structure design scheme in this paper can also provide reference values for relative image restoration tasks based on deep learning.

Key words: Convolutional neural networks, Deep learning, Feature fusion, Image de-raining