Encoder-Decoder Network with Guided Transmission Map: Architecture
This work addresses image quality enhancement for computer vision applications, but it is incremental as it builds on existing U-Net and dark channel prior methods.
The paper tackles single image dehazing by proposing the Encoder-Decoder Network with Guided Transmission Map (EDN-GTM), which uses a hazy RGB image and a transmission map from dark channel prior as inputs, achieving state-of-the-art results on benchmark datasets with improved PSNR and SSIM metrics.
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.