Video Dehazing Based on Convolutional Neural Network Driven Using Our Collected Dataset
Ziyan Wang · Journal of Physics Conference Series · 2020
With the fast development of automatic driving and video monitoring applications, video dehazing is a vital problem in computer vision because turbid images can have a large impact on their performance. As one of the remarkable techniques, convolutional neural networks have been greatly developed and shown great effectiveness in video dehazing at present. Inspired by deep learning, this paper introduces one of the convolutional neural networks named AOD-Net and collects a dataset to evaluate its dehazing performance. By comparison, the proposed algorithm achieves high SSIM and high PSNR on both our collected dataset and NYU Depth V2 dataset. AOD-Net not only generates well-dehazed images, but also learns very quickly during the training process. This All-in-One Dehazing Network improves visual quality of hazy images to a large extent.