Image Dehazing Algorithm Based on Convolutional Neural Network and Dynamic Ambient Light

To effectively estimate the transmittance of the hazy images and improve the darkness of the fog removal image, an image dehazing algorithm is proposed based on convolutional neural network and dynamic ambient light. Firstly, a transmittance estimation network is designed based on convolutional neural network. Then, an image library containing paired real hazy images and transmittance images is constructed. And randomly block sampling is performed to obtain the paired hazy patches and transmittance patches which arc used as training sets for training the transmittance estimation network. After that, the trained network is used to estimate the transmittance of hazy images and then smooth the acquired transmittance. At the same time, considering the problem of uneven illumination of images, dynamic ambient light is used to replace global atmospheric light. Finally, the smooth filtered transmittance and dynamic ambient light arc used to restore the images. Experimental results show that the algorithm can not only effectively restore the images, but also significantly improve the brightness and saturation of the restored images.