论文标题
一个新颖的编码器 - 码头网络，带有指导传输图，用于单图像
A Novel Encoder-Decoder Network with Guided Transmission Map for Single Image Dehazing
论文作者
论文摘要
本文提出了一个具有指导传输图（EDN-GTM）的新型编码器 - 模型网络（EDN-GTM）。拟议的EDN-GTM将传统的RGB朦胧图像与其传输图一起估计，该图像通过采用暗通道作为网络的输入而估计。拟议的EDN-GTM将U-NET用于图像分割，作为核心网络，并利用各种修改，包括空间金字塔池池模块和Swish激活，以实现最新的脱壳性能。基准数据集上的实验表明，根据PSNR和SSIM指标，所提出的EDN-GTM优于大多数传统和深度学习的图像飞行方案。拟议的EDN-GTM此外，还证明了其对对象检测问题的适用性。具体而言，当应用于图像预处理工具进行驱动对象检测时，提出的EDN-GTM可以有效地消除雾度，并在MAP度量方面将检测准确性显着提高4.73％。该代码可在以下网址获得：https：//github.com/tranleanh/edn-gtm。
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.
