  A novel Encoder-Decoder Network with Guided Transmission Map (EDN-GTM) for
single image dehazing scheme is proposed in this paper. The proposed EDN-GTM
takes conventional RGB hazy image in conjunction with its transmission map
estimated by adopting dark channel prior as the inputs of the network. The
proposed EDN-GTM utilizes U-Net for image segmentation as the core network and
utilizes various modifications including spatial pyramid pooling module and
Swish activation to achieve state-of-the-art dehazing performance. Experiments
on benchmark datasets show that the proposed EDN-GTM outperforms most of
traditional and deep learning-based image dehazing schemes in terms of PSNR and
SSIM metrics. The proposed EDN-GTM furthermore proves its applicability to
object detection problems. Specifically, when applied to an image preprocessing
tool for driving object detection, the proposed EDN-GTM can efficiently remove
haze and significantly improve detection accuracy by 4.73% in terms of mAP
measure. The code is available at: https://github.com/tranleanh/edn-gtm.
