[CVPR 2023] | RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors

[ACCV22] Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal, https://arxiv.org/abs/2210.03061

[CVPR 2022] Learning Multiple Adverse Weather Removal via Two-stage Knowledge Learning and Multi-contrastive Regularization: Toward a Unified Model

Code for Blind Image Decomposition (BID) and Blind Image Decomposition network (BIDeN). ECCV, 2022.

A Python2 implementation of single image haze removal

Single Image Dehazing with a Generic Model-Agnostic Convolutional Neural Network

Transfer DeBlurGan to dehaze

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.

This is the project page of our paper which has been published in ECCV 2020.

NeurIPS 2021 paper: Learning to Dehaze with Polarization

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

Dataset and code of our AAAI2022 paper "Transmission-Guided Bayesian Generative Model for Smoke Segmentation"

A python package of robust and effective defogging/dehazing method

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

This is an python implementation of "single image haze removal using dark channel prior"

实现了图片处理功能的平台，完成了数据库的持久化存储

He's Single Image Haze Removal

数字媒体作业

Weather image noise (rain, snow, haze) removal with classificatio

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.