Encoder–Decoder Network with Guided Transmission Map: Robustness and Applicability

Abstract

The robustness and applicability of the Encoder–Decoder Network with Guided Transmission Map (EDN-GTM) proposed for efficient single image dehazing purpose are examined in this paper. The EDN-GTM utilizes the transmission map extracted by dark channel prior approach as an additional input channel of a novel U-Net-based generative network to achieve an improved dehazing performance. The EDN-GTM has shown a very favorable performance compared with most recently proposed dehazing schemes including both traditional and deep learning-based ones in terms of PSNR and SSIM metrics. To further validate the robustness and applicability of the EDN-GTM scheme, extensive experiments and quantitative evaluations on various benchmark datasets are conducted in this paper. In terms of robustness, experimental results on different benchmark dehazing datasets such as Dense-HAZE, NH-HAZE, and D-HAZY show that the EDN-GTM scheme consistently outperforms most modern dehazing approaches on both synthetic and realistic hazy data regardless of scene locations: indoor or outdoor. On the other hand, experiments on WAYMO and Foggy Driving datasets imply that the EDN-GTM can be effectively applied as an image pre-processing tool to object detection tasks in autonomous driving systems.