Abstract: 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.

Key words: depth estimation, single hazy image, perceptual loss function, convolutional neural network, computer vision