密集连接扩张卷积神经网络的单幅图像去雾

针对大多数图像去雾算法模型参数估计准确性差及色彩失真等问题,提出了一种端到端的密集连接扩张卷积神经网络。首先,通过使用多层密集连接结构来增加网络的特征利用率,避免网络加深时的梯度消失现象。其次,通过在密集块中使用不同扩张率的扩张卷积,使网络在充分聚合上下文特征信息时不损失空间分辨率,并避免了网格伪影的产生。最后,为了提高算法的去雾能力,将该网络划分为多个阶段,并在每个阶段引入侧输出模块,从而获得更精确的特征信息。实验结果表明,所提出的去雾算法无论是在合成数据集上还是在真实数据集上都取得了较好的去雾效果,恢复的色彩更接近无雾图像,并且定量评价指标峰值信噪比(PSNR)和结构相似性(SSIM)均优于其他对比方法。

Single Image Dehazing Method Based on Densely Connected Dilated Convolutional Neural Network

In view of the poor accuracy of model parameter estimation and color distortion in most image dehazing algorithms,an end-to-end dense connected dilated convolutional neural network is proposed.First of all,this paper uses multi-layer dense connection structure to increase the feature utilization of the network and avoid the gradient disappearance when the network deepens.Secondly,by using the dilation convolution with different dilation rates in the dense blocks,the network can fully aggregate the context feature information without losing the spatial resolution,and avoid the generation of mesh artifacts.Finally,in order to improve the ability of the algorithm,this paper divides the network into several stages,and introduces the side output module in each stage,so as to obtain more accurate feature information.The experimental results show that the proposed dehazing algorithm has a good dehazing effect on both the synthetic data set and the real data set.The recovered color is closer to the ground truth,and the quantitative evaluation indexes peak signal to noise ratio(PSNR)and structural similarity index(SSIM)are better than other comparison methods.