ip-dehaze Goto Github PK
Type: Organization
Type: Organization
PyTorch implementation for You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network (YOLY) (IJCV 2021)
Inference code for "Aerial Image Dehazing with Attentive Deformable Transformers" [WACV-23]
A collection of dehazing methods.
BeDDE, a real-world benchmark dataset for dehazing, for "Dehazing Evaluation: Real-world Benchmark Datasets, New Criteria and Baselines" (TIP 2020)
[CVPR 2023] Curricular Contrastive Regularization for Physics-aware Single Image Dehazing
Code for the paper "Rethinking the Elementary function fusion for Single-Image Dehazing"
Implementation of CVPR2022 "Self-augmented Unpaired Image Dehazing via Density and Depth Decomposition"
[AAAI2024] Decoupling Degradations with Recurrent Network for Video Restoration in Under-Display Camera.
Official implementation of DEA-Net
This is the official PyTorch implementation of DehazeDCT. Our method achieves the second best performance in NTIRE 2024 Dense and NonHomogeneous Dehazing Challenge (CVPR workshop))
[CVPR 2024] Driving-Video Dehazing with Non-Aligned Regularization for Safety Assistance
ECCV'22 Oral | Perceiving and Modeling Density for Single Image Dehazing.
FFA-Net: Feature Fusion Attention Network for Single Image Dehazing
[ACCV22] Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal, https://arxiv.org/pdf/2210.03061.pdf
[CVPR'23] Video Dehazing via a Multi-Range Temporal Alignment Network with Physical Prior
[CVPR 2022 Oral] Official repository for "MAXIM: Multi-Axis MLP for Image Processing". SOTA for denoising, deblurring, deraining, dehazing, and enhancement.
[CVPR 2022 Oral] PyTorch re-implementation for "MAXIM: Multi-Axis MLP for Image Processing", with *training code*. Official Jax repo: https://github.com/google-research/maxim
The source code of CVPR 2020 paper "Multi-Scale Boosted Dehazing Network with Dense Feature Fusion"
A Simple Case of Image Dehazing with the Help of NAS.
[ACMMM2023] "Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution", https://arxiv.org/abs/2308.01738
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