Convolutional Autoencoder For Single Image Dehazing
Rongsen Chen, Edmund M-K Lai · 2019
In this paper, we present a Convolutional AutoEncoder (CAE) for single image dehazing. Our CAE makes use of Densely Connection Networks as its encoder and decoder. It is trained with the corresponding hazy and clean images at the input and output, enabling it to remove the haze without having to rely on an atmospheric scattering model. The CAE is trained and tested with the RESIDE dataset. Experiment results show that this CAE outperforms eight state-of-art methods. The trained CAE is also applied to some real-life hazy images, and decent dehazing results are obtained. Moreover, our method is computationally efficient enough to run on computers without GPU units.