GADO-Net: An Improved AOD-Net Single Image Dehazing Algorithm

With the development of modern industry, the frequency of special weather such as haze has increased, and images taken on foggy days will have blurred picture details, reduced contrast, and partial loss of image information, making subsequent image processing difficult. In previous studies, many scholars have proposed many algorithms with excellent performance to solve such problems, and the more widely used one is AOD-Net. However, there are a series of problems such as easy overfitting of the joint estimation of parameters during training, too small sensory field, and poor defogging effect of low-illumination images. The proposed algorithm—GADO-Net uses a depth-separable convolutional neural network instead of a 5-layer convolutional neural network for joint estimation of atmospheric light values and transmittance, while adding a pyramidal pooling module, which makes the algorithm have the advantages of extending the perception field to a certain extent, effectively improving the overfitting phenomenon during training, and improving the ability of the algorithm to fetch global information of foggy sky images. Finally, in this algorithm, the Peak Signal to Noise Ratio (PSNR) is significantly improved by using MS-SSIM and L1 weighting as the LOSS function and using optimization search to obtain the optimal parameters of the model. The experimental show that GAOD-Net performs better than the DCP (Dark Channel Prior), histogram equalization algorithm and conventional AOD-Net in terms of SSE and PSNR, and can dehazing foggy images more effectively.