Alternatives and similar repositories for PDI2A-CVPR2022

Users that are interested in PDI2A-CVPR2022 are comparing it to the libraries listed below

• yuhuUSTC / FSDGN
  Official implementation for "Frequency and Spatial Dual Guidance for Image Dehazing" [ECCV 2022]
  ☆62Updated 2 years ago
• aupendu / zero-restore
  Zero-shot Single Image Restoration through Controlled Perturbation of Koschmieder's Model
  ☆44Updated 3 years ago
• liboyun / ZID
  Pytorch implementation of Zero-Shot Image Dehazing (ZID) (TIP 2020)
  ☆25Updated 4 years ago
• Owen718 / NightHazeFormer
  ACM MM'23 | The office repository of NightHazeFormer: Single Nighttime Haze Removal Using Prior Query Transformer
  ☆25Updated last year
• ChangfengYu-Hust / UDGNet
  Unsupervised Image Deraining: Optimization Model Driven Deep CNN
  ☆16Updated 3 years ago
• weitingchen83 / ICCV2021-Single-Image-Desnowing-HDCWNet
  This paper is accepted by ICCV 2021.
  ☆72Updated last year
• csbhr / SGID-PFF
  The repository is an official implementation of our TIP paper : Self-Guided Image Dehazing Using Progressive Feature Fusion
  ☆28Updated 3 years ago
• Oliiveralien / CPNet
  Context-aware Pretraining for Efficient Blind Image Decomposition (CVPR'23)
  ☆26Updated 2 years ago
• zzr-idam / 4KDehazing
  ☆91Updated 2 years ago
• liuye123321 / DMT-Net
  Image dehazing
  ☆49Updated 4 years ago
• CharlieZCJ / UTVNet
  Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement (ICCV2021)
  ☆51Updated 8 months ago
• XLearning-SCU / 2021-IJCV-YOLY
  PyTorch implementation for You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network (YOLY) (IJCV 2021)
  ☆67Updated 3 years ago
• UCLA-VMG / GT-RAIN
  ☆36Updated 2 years ago
• chaimi2013 / 3R
  Nighttime Dehazing with a Synthetic Benchmark (ACM MM 2020)
  ☆62Updated 3 years ago
• Joyies / SPDNet
  [ICCV2021] Official code for Structure-Preserving Deraining with Residue Channel Prior Guidance
  ☆55Updated 3 years ago
• KevinJ-Huang / Stereo-Low-Light
  TMM 2022 (Official Implementation of "Low-light Stereo Image Enhancement")
  ☆14Updated 2 years ago
• MinfengZhu / EEMEFN
  Low-Light Image Enhancement via Edge-Enhanced Multi-Exposure Fusion Network
  ☆52Updated 4 years ago
• zsyOAOA / S2VD
  Semi-supervised Video Deraining with Dynamical Rain Generator (CVPR, 2021, Pytorch)
  ☆77Updated 4 years ago
• LI-Hao-SJTU / DerainRLNet
  Representation learning net (RLNet) for deraining purpose
  ☆23Updated 3 years ago
• HDCVLab / Stereo-Image-Deraining
  ☆16Updated 2 years ago
• xiaofeng94 / RefineDNet-for-dehazing
  The Pytorch implementation for "RefineDNet: A Weakly Supervised Refinement Framework for Single Image Dehazing" (TIP 2021)
  ☆85Updated 3 years ago
• hongwang01 / VRGNet
  【CVPR 2021, Variational Rain Generator】 From Rain Generation to Rain Removal
  ☆66Updated last year
• KevinJ-Huang / FECNet
  ECCV 2022 (Official implementation of "Deep Fourier-based Exposure Correction Network with Spatial-Frequency Interaction")
  ☆82Updated last year
• XLearning-SCU / 2020-TIP-ZID
  Pytorch implementation of Zero-Shot Image Dehazing (ZID) (TIP 2020)
  ☆16Updated 4 years ago
• iCVTEAM / DehazeFlow
  DehazeFlow: Multi-scale Conditional Flow Network for Single Image Dehazing. ACM Conference on Multimedia (ACM MM), 2021
  ☆21Updated 2 years ago
• weitingchen83 / JSTASR-DesnowNet-ECCV-2020
  This is the project page of our paper which has been published in ECCV 2020.
  ☆30Updated last year
• ShenZheng2000 / SAPNet-for-image-deraining
  Official Pytorch Implementation for "SAPNet: Segmentation-Aware Progressive Network for Perceptual Contrastive Image Deraining"
  ☆28Updated 2 years ago
• YaN9-Y / D4
  Implementation of CVPR2022 "Self-augmented Unpaired Image Dehazing via Density and Depth Decomposition"
  ☆114Updated 2 years ago
• KarelZhang / CSDNet-CSDGAN
  [TNNLS 2021] The official code for the paper "Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement"
  ☆29Updated 4 years ago
• FadeoN / Distilling-Image-Dehazing-With-Heterogeneous-Task-Imitation
  ☆50Updated 5 years ago