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(2017) Joint Transmission Map Estimation and Dehazing using Deep Networks

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Zhang H, Sindagi V, Patel V M. Joint transmission map estimation and dehazing using deep networks[J]. arXiv preprint arXiv:1708.00581, 2017.


1. Overview

大多数现有的方法假设constant atmosphere light，包含两个步骤

  • 基于prior-based方法估计transmission map
  • 近似解计算haze-free image

论文提出multi-task结构

  • Relax constant atmosphere light assumption, joint estimate transmission map and de-hazing
  • Introduce GAN
  • Introduce perceptual loss
  • DehazeNet
  • Multi-scale Net

1.2. Model



  • PReLU
  • Generator使用U-Net结构

1.3. Loss Function

  • Transmission Map Loss


  • Dehazing Loss
    Perceptual loss (VGG-16 relu3_1)


1.4. 速度

512x512. 18 fps

1.5. 数据集

α ∈ [0.5, 1.2]. β ∈ [0.4, 1.6]

1.5.1. NYU Depth Dataset

  • Training Set. 600 images x 4
  • Testing Set. 300 images x 4
  • Real Set. 30 images


2. Experiments

2.1. AblationStudy

  • Adversarial Loss



  • Perceptual Loss



  • Euclidean Loss



  • Transmission Map



2.2. 实验结果