[转] DehazeZoo (Single Image vs. Video Based) 去雾评价指标、数据集及算法总结

DehazeZoo (Single Image vs. Video Based)

[转载] https://www.ctolib.com/cxtalk-DehazeZoo.html

Xiang Chen, Yufeng Li, Yufeng Huang

1 Description

  • DehazeZoo: A survey on haze removal from video and single image. Papers, codes and datasets are maintained.

  • Thanks for the sharing of DerainZoo by Youzhao Yang.

  • More details about image dehazing and deraining are available here.

2 Image Quality Metrics

  • PSNR (Peak Signal-to-Noise Ratio) [paper] [matlab code] [python code]
  • SSIM (Structural Similarity) [paper] [matlab code] [python code]
  • VIF (Visual Quality) [paper] [matlab code]
  • FSIM (Feature Similarity) [paper] [matlab code]
  • NIQE (Naturalness Image Quality Evaluator) [paper][matlab code][python code]

3 Dehazing Research

3.1 Datasets


  • KITTI [paper][dataset]
  • RESIDE [paper][dataset]
  • SceneNet [paper][dataset]
  • I-HAZE [paper][dataset]
  • O-HAZE [paper][dataset]
  • D-HAZY [paper][dataset]
  • Middlebury [paper][dataset]
  • NYU Depth Dataset V2 [paper][dataset]

3.2 Papers


2020

  • Pang et al, BidNet: Binocular Image Dehazing without Explicit Disparity Estimation. (CVPR) [paper][code]
  • Sourya et al, Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing. [paper][code]
  • Dong et al, Multi-Scale Boosted Dehazing Network with Dense Feature Fusion. (CVPR) [paper][code]
  • Li et al, Learning to Dehaze From Realistic Scene with A Fast Physics Based Dehazing Network. [paper][code]
  • Shao et al, Domain Adaptation for Image Dehazing. (CVPR) [paper][code][web]
  • Wu et al, Accurate Transmission Estimation for Removing Haze and Noise from a Single Image. (TIP) [paper][code]
  • Ren et al, Single Image Dehazing via Multi-Scale Convolutional Neural Networks with Holistic Edges. (IJCV) [paper][code]
  • Dong et al, FD-GAN: Generative Adversarial Networks with Fusion-discriminator for Single Image Dehazing. [paper][code]
  • Qin et al, FFA-Net: Feature Fusion Attention Network for Single Image Dehazing. (AAAI) [paper][code]

2019

  • Wu et al, Learning Interleaved Cascade of Shrinkage Fields for Joint Image Dehazing and Denoising. (TIP) [paper][code]
  • Li et al, Semi-Supervised Image Dehazing. (TIP) [paper][code]
  • Li et al, Benchmarking Single Image Dehazing and Beyond. (TIP) [paper][code][web]
  • Pei et al, Classification-driven Single Image Dehazing. [paper][code]
  • Liu et al, GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing. (ICCV) [paper][code]
  • Li et al, Joint haze image synthesis and dehazing with mmd-vae losses. [paper][code]
  • Peter et al, Feature Forwarding for Efficient Single Image Dehazing. [paper][code]
  • Shu et al, Variational Regularized Transmission Refinement for Image Dehazing. [paper][code]
  • Liu et al, End-to-End Single Image Fog Removal using Enhanced Cycle Consistent Adversarial Networks. [paper][code]
  • Chen et al, Gated Context Aggregation Network for Image Dehazing and Deraining. (WACV) [paper][code]
  • Ren et al, Deep Video Dehazing with Semantic Segmentation. (TIP) [paper][code]

2018

  • Ren et al, Gated Fusion Network for Single Image Dehazing. (CVPR) [paper][code][web]
  • Zhang et al, FEED-Net: Fully End-To-End Dehazing. (ICME) [paper][code]
  • Zhang et al, Densely Connected Pyramid Dehazing Network. (CVPR) [paper][code]
  • Yang et al, Towards Perceptual Image Dehazing by Physics-based Disentanglement and Adversarial Training. (AAAI) [paper][code]
  • Deniz et al, Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing. (CVPRW) [paper][code]

Before

  • Ren et al, An All-in-One Network for Dehazing and Beyond. (ICCV) [paper][code][web]
  • Zhu et al, Single Image Dehazing via Multi-Scale Convolutional Neural Networks. (ECCV) [paper][code][web]
  • Cai et al, DehazeNet: An end-to-end system for single image haze removal. (TIP) [paper][code][web]
  • Ren et al, A fast single image haze removal algorithm using color attenuation prior. (TIP) [paper][code]
  • He et al, Single Image Haze Removal Using Dark Channel Prior. (CVPR) [paper][code]

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