The Official Implementation for "HAIR: Hypernetworks-based All-in-One Image Restoration".
Code for Blind Image Decomposition (BID) and Blind Image Decomposition network (BIDeN). ECCV, 2022.
Dehazing using multiscale(processing) dark channel prior
[ACCV22] Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal, https://arxiv.org/abs/2210.03061
This is the source code of PMS-Net: Robust Haze Removal Based on Patch Map for Single Images which has been published in CVPR 2019 Long Beach
This is the project page of our paper which has been published in ECCV 2020.
Dataset and code of our AAAI2022 paper "Transmission-Guided Bayesian Generative Model for Smoke Segmentation"
In this Project, important algorithms such as Canny Edge Detection, Harris Corner Detection, Segmentation, and Dehazing are utilized. These algorithms perform operations like detecting edges and corners in images, segmenting different regions, and enhancing foggy or blurred images.
[CVPR 2023] | RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors
This is the source code of PMHLD-Patch-Map-Based-Hybrid-Learning-DehazeNet-for-Single-Image-Haze-Removal which has been accepted by IEEE Transaction on Image Processing 2020.
[CVPR 2022] Learning Multiple Adverse Weather Removal via Two-stage Knowledge Learning and Multi-contrastive Regularization: Toward a Unified Model
NeurIPS 2021 paper: Learning to Dehaze with Polarization
This is an python implementation of "single image haze removal using dark channel prior"
Lightweight and Efficient Image Dehazing Network Guided by Transmission Estimation from Real-world Hazy Scenes; accepted by Sensors 2021, 21(3), 960, MDPI; https://doi.org/10.3390/s21030960
Single Image Dehazing with a Generic Model-Agnostic Convolutional Neural Network
A Python2 implementation of single image haze removal
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