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A novel Encoder-Decoder Network with Guided Transmission Map (EDN-GTM) forsingle image dehazing scheme is proposed in this paper. The proposed EDN-GTMtakes conventional RGB hazy image in conjunction with its transmission mapestimated by adopting dark channel prior as the inputs of the network. Theproposed EDN-GTM utilizes U-Net for image segmentation as the core network andutilizes various modifications including spatial pyramid pooling module andSwish activation to achieve state-of-the-art dehazing performance. Experimentson benchmark datasets show that the proposed EDN-GTM outperforms most oftraditional and deep learning-based image dehazing schemes in terms of PSNR andSSIM metrics. The proposed EDN-GTM furthermore proves its applicability toobject detection problems. Specifically, when applied to an image preprocessingtool for driving object detection, the proposed EDN-GTM can efficiently removehaze and significantly improve detection accuracy by 4.73% in terms of mAPmeasure. The code is available at: https://github.com/tranleanh/edn-gtm.

A Novel Encoder-Decoder Network with Guided Transmission Map for Single Image Dehazing