融合感知损失的单幅雾霾图像深度估计

针对雾霾情况下室内外图像深度难以估计的问题,提出了融合感知损失函数的单幅雾霾图像深度估计方法.首先采用双尺度网络模型对雾霾图像进行粗提取,再结合底层特征进行局部细化;然后在上采样阶段使用多卷积核上采样方法,得到雾霾图像的预测深度图;最后将像素级损失函数与感知损失函数结合构造新的复合损失函数,对网络进行训练.在室内NYU...

Single hazy image depth estimation fusing the perceptual loss function

To address the difficulty of estimating the indoor and outdoor depth under hazy conditions,a single hazy image depth estimation method fusing the perceptual loss function is proposed. Firstly, a two-scale network model is used to coarsely extract the hazy images that are are then locally refined by combining the underlying features. Then, in the upsampling stage, a multi-convolution kernel upsampling method is used to obtain the haze image predition depth map. Finally, the network is trained by combining the pixel-level loss function and the perceptual loss function into a new composite loss function. The experiments are trained, tested, and validated on the indoor NYU Depth v2 dataset and the outdoor Make3D dataset. The result shows that the two-scale network model combining the multi-convolutional kernel up-sampling method and the composite loss function can better estimate the depth information of a single hazy image, improve the accuracy and quality of depth estimation under hazy condition, shorten the training time of the model,and improve the applicability and accuracy of the depth estimation of hazy images.