haze-removal
There are 41 repositories under haze-removal topic.
26hzhang/OptimizedImageEnhance
Several image/video enhancement methods, implemented by Java, to tackle common tasks, like dehazing, denoising, backscatter removal, low illuminance enhancement, featuring, smoothing and etc.
jinyeying/nighttime_dehaze
[ACMMM2023] "Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution", https://arxiv.org/abs/2308.01738
fingerk28/Two-stage-Knowledge-For-Multiple-Adverse-Weather-Removal
[CVPR 2022] Learning Multiple Adverse Weather Removal via Two-stage Knowledge Learning and Multi-contrastive Regularization: Toward a Unified Model
Utkarsh-Deshmukh/Single-Image-Dehazing-Python
python implementation of the paper: "Efficient Image Dehazing with Boundary Constraint and Contextual Regularization"
weitingchen83/ICCV2021-Single-Image-Desnowing-HDCWNet
This paper is accepted by ICCV 2021.
xw-hu/DAF-Net
Depth-Attentional Features for Single-Image Rain Removal and RainCityscapes Dataset | CVPR 2019
Rock-100/Single-Image-Haze-Removal-Using-Dark-Channel-Prior
[CVPR 2009] Single Image Haze Removal Using Dark Channel Prior
kindraywind/SingleImageHazeRemover
A Python2 implementation of single image haze removal
rzwm/IBEABFHR
Image Brightness Enhancement Automatically Based on Fast Haze Removal
tranleanh/edn-gtm
EDN-GTM Scheme for Single Image Dehazing
weitingchen83/Dehazing-PMHLD-Patch-Map-Based-Hybrid-Learning-DehazeNet-for-Single-Image-Haze-Removal-TIP-2020
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.
weitingchen83/JSTASR-DesnowNet-ECCV-2020
This is the project page of our paper which has been published in ECCV 2020.
xw-hu/DGNL-Net
DGNL-Net and RainCityscapes
JoshuaEbenezer/cwgan
Conditional Wasserstein Generative Adversarial Network for image-to-image translation.
yenshih/Dehaze
A cross-platform image dehazing/defogging mobile app implemented with React Native, Djinni and OpenCV, based on dark channel prior and fast guided filter.
DOUDIU/Hardware-Implementation-of-the-Dark-Channel-Prior-Haze-Removal-Algorithm
The Dark Channel Prior technique is implemented on FPGA using only Verilog code and no Intellectual Property, making it convenient to replicate using any simulator and any of the available FPGA boards, including those from Xilinx and Altera.
weitingchen83/PMS-Net
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
flaviaratto/Single-Image-Haze-removal-using-Dark-Channel-Prior-and-Guided-Image-Filtering-
A Matlab implementation of haze removal from a single image (RGB and Grayscale)
bghojogh/Haze-Removal-Dark-Channel-Prior
The code for haze removal using dark channel prior, which was a part of the self-driving car project
san-santra/dehaze_t_comparator
Code of the paper "Learning a Patch Quality Comparator for Single Image Dehazing"
rexledesma/Haze-Removal
An implementation of "Single Image Haze Removal Using Dark Channel Prior" by He et al. 2009
datngo93/mIFDH
This is the MATLAB source code of a haze removal algorithm, which dehazes a hazy input image using simple image enhancement techniques, such as detail enhancement, gamma correction, and single-scale image fusion.
YyzHarry/ImgSensingNet
[INFOCOM 2019] ImgSensingNet: UAV Vision Guided Aerial-Ground Air Quality Sensing System
datngo93/OTM-AAL
This is the MATLAB source code of a haze removal algorithm published in Remote Sensing (MDPI) under the title "Robust Single-Image Haze Removal Using Optimal Transmission Map and Adaptive Atmospheric Light". The transmission map was estimated by maximizing an objective function quantifying image contrast and sharpness. Additionally, an adaptive atmospheric light was devised to prevent the loss of dark details after removing haze.
ZQPei/Haze_Removal_python
This is an python implementation of "single image haze removal using dark channel prior"
mshr-h/Image_Dehazing
Haze removal using Dark channel prior
samibinsami/A-Novel-Image-Dehazing-and-Assessment-Method
This is the implementation of the dehazing algorithm proposed in IBA-ICICT conference 2019
SidCVision/color-constancy-prior
An Improved Air-Light Estimation Scheme for Single Haze Images Using Color Constancy Prior.
Vaibhav-Rathod/Under-Water-Image-Enhancement-By-SRCNN
An underwater image enhancement method and a corresponding image super-resolution algorithm. Image enhancement Technique. Super-resolution Convolutional neural networks the Retinex algorithm gamma correction. Dark prior
datngo93/ICAP
This is a MATLAB source code of the paper "Improved Color Attenuation Prior for Single-Image Haze Removal", published in Applied Sciences-Basel (MDPI).
celestial-shubham/Image-Dehazing-Project
Haze degrades image quality and limits visibility especially in outdoor settings. This consequently affects performance on other high-level tasks such as object detection and recognition. The AOD network proposed by Boyi Li et. al. is an end-to-end CNN to de-haze an image. AOD takes as input a hazy image and generates a de-hazed image. Here i have implement the given paper AOD-net in Tensorflow.
datngo93/Haziness-degree-evaluator
This is the MATLAB implementation of the haziness degree evaluator for predicting the haze density from a single image. The relevant work was published in the MDPI Sensors journal under the title "Haziness degree evaluator: a knowledge-driven approach for haze density estimation".
praveenkulkarni1996/hazeremoval
Haze Removal tool using Dark Channel Prior. Based on work by Kaiming He.
ShadyZOZ/haze-removal
Single Image Haze Removal Using Dark Channel Prior
eshaagarwa/Image-dehazing
🌫️ Haze Removal with Dark Prior Channel 🌫️ "We’re tackling hazy images using the Dark Prior Channel method, which clears haze, dust, and fog by analyzing pixel intensity. 🚀 While we’ve seen promising results, limited resources impact our full dehazing capability. 🖼️✨ Our work enhances image clarity and contributes to haze removal techniques."
tranleanh/sddn
Distillation of Efficient Dehazing Networks via Soft Knowledge