mlsanityData to 14 Oct 2026

Encoder-Decoder Network with Guided Transmission Map: Architecture

arXiv 2212.05936 ↗First posted 7 Dec 2022cs.CV eess.IVcited by 1

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 14 references that are in the map. First posted on arXiv 7 Dec 2022.

Builds on

4 of 14 references.

References · 4

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2016DehazeNet: An End-to-End System for Single Image Haze Removalcited by 314
  3. 2020Multi-Scale Boosted Dehazing Network with Dense Feature Fusioncited by 230
  4. 2021Contrastive Learning for Compact Single Image Dehazingcited by 219

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Citing papers per year: 2023: 1.

1 paper.