DehazeZoo (Single Image vs. Video Based)

By Xiang Chen, Jinshan Pan, 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

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• 3R [paper][dataset]
• KITTI [paper][dataset]
• RESIDE [paper][dataset]
• HazeRD [paper][dataset]
• SceneNet [paper][dataset]
• I-HAZE [paper][dataset]
• O-HAZE [paper][dataset]
• D-HAZY [paper][dataset]
• Middlebury [paper][dataset]
• 3DRealisticScene [paper][dataset]
• NYU Depth Dataset V2 [paper][dataset]
• 4K-Dehazing Data [paper][dataset](Extraction code：7x3l)

3.2 Papers

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2021

• Liu et al, From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data. (ACMMM) [paper][code]
• Chen et al, PSD: Principled Synthetic-to-Real Dehazing Guided by Physical Priors. (CVPR) [paper][code]
• Zheng et al, Ultra-High-Defifinition Image Dehazing via Multi-Guided Bilateral Learning. (CVPR) [paper][code]
• Wu et al, Contrastive Learning for Compact Single Image Dehazing. (CVPR) [paper][code]
• Shyam et al, Towards Domain Invariant Single Image Dehazing. (AAAI) [paper][code]
• Liu et al, Indirect Domain Shift for Single Image Dehazing. [paper][code]
• Yi et al, Two-Step Image Dehazing with Intra-domain and Inter-domain Adaption. [paper][code]

2020

• Dong et al, Physics-based Feature Dehazing Networks. (ECCV) [paper][code]
• Deng et al, HardGAN: A Haze-Aware Representation Distillation GAN for Single Image Dehazing. (ECCV) [paper][code]
• Anvari et al, Dehaze-GLCGAN: Unpaired Single Image De-hazing via Adversarial Training. [paper][code]
• Zhang et al, Nighttime Dehazing with a Synthetic Benchmark. [paper][code]
• Kar et al, Transmission Map and Atmospheric Light Guided Iterative Updater Network for Single Image Dehazing. (CVPR) [paper][code]
• Shen et al, Implicit Euler ODE Networks for Single-Image Dehazing. [paper][code]
• Liu et al, Efficient Unpaired Image Dehazing with Cyclic Perceptual-Depth Supervision. [paper][code]
• Li et al, You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network. [paper][code]
• 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

• Li 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]
• Zhu 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]

4 Note

• The above content is constantly updated, welcome continuous attention!

5 Contact

• If you have any question, please feel free to contact Xiang Chen (Email: [email protected]).