An insight into the architecture of the Encoder-Decoder Network with Guided Transmission Map (EDN-GTM), a novel and effective single image dehazing scheme, is presented in this paper. The EDN-GTM takes a conventional RGB hazy image in conjunction with the corresponding transmission map estimated by the dark channel prior (DCP) approach as inputs of the network. The EDN-GTM adopts an enhanced structure of U-Net developed for dehazing tasks and the resulting EDN-GDM has shown state-of-the-art performances on benchmark dehazing datasets in terms of PSNR and SSIM metrics. In order to give an in-depth understanding of the well-designed architecture which largely contributes to the success of the EDN-GTM, extensive experiments and analysis from selecting the core structure of the scheme to investigating advanced network designs are presented in this paper.
翻译:本文介绍了Encoder-Decoder网络与引导传输图(简称EDN-GTM)的体系结构,这是一种新颖有效的单张图像除雾方案。EDN-GTM将传统的RGB雾霾图像与通过暗通道先验(DCP)方法估算出的对应传输图像一起作为网络的输入。EDN-GTM采用了增强版的U-Net结构,用于除雾任务,最终结果表明,在PSNR和SSIM度量方面,EDN-GTM的性能优于基准除雾数据集上的其他算法。为了深入理解这种设计精良的体系结构,这篇文章从选择方案的核心结构到研究先进网络设计进行了广泛的实验和分析。