Unsupervised single image dehazing usingmultiple scattering model

2021 ◽  
Author(s):  
Shunmin An ◽  
Xixia Huang ◽  
Linling Wang ◽  
Zhangjing Zheng ◽  
Le Wang
Keyword(s):  
Single Image ◽  
Image Dehazing ◽  
Scattering Model ◽  
Single Image Dehazing

scholarly journals A Single Image Dehazing Method Using Average Saturation Prior

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Zhenfei Gu ◽  
Mingye Ju ◽  
Dengyin Zhang
Keyword(s):  
Estimation Accuracy ◽  
Single Image ◽  
High Definition ◽  
Image Dehazing ◽  
Scattering Model ◽  
Atmospheric Scattering ◽  
Proposed Model ◽  
Bad Weather ◽  
Atmospheric Scattering Model ◽  
Single Image Dehazing

Outdoor images captured in bad weather are prone to yield poor visibility, which is a fatal problem for most computer vision applications. The majority of existing dehazing methods rely on an atmospheric scattering model and therefore share a common limitation; that is, the model is only valid when the atmosphere is homogeneous. In this paper, we propose an improved atmospheric scattering model to overcome this inherent limitation. By adopting the proposed model, a corresponding dehazing method is also presented. In this method, we first create a haze density distribution map of a hazy image, which enables us to segment the hazy image into scenes according to the haze density similarity. Then, in order to improve the atmospheric light estimation accuracy, we define an effective weight assignment function to locate a candidate scene based on the scene segmentation results and therefore avoid most potential errors. Next, we propose a simple but powerful prior named the average saturation prior (ASP), which is a statistic of extensive high-definition outdoor images. Using this prior combined with the improved atmospheric scattering model, we can directly estimate the scene atmospheric scattering coefficient and restore the scene albedo. The experimental results verify that our model is physically valid, and the proposed method outperforms several state-of-the-art single image dehazing methods in terms of both robustness and effectiveness.

scholarly journals DehazeGAN: When Image Dehazing Meets Differential Programming

2018 ◽  
Author(s):  
Hongyuan Zhu ◽  
Xi Peng ◽  
Vijay Chandrasekhar ◽  
Liyuan Li ◽  
Joo-Hwee Lim
Keyword(s):  
Physical Parameters ◽  
Single Image ◽  
Image Dehazing ◽  
Generative Adversarial Network ◽  
Scattering Model ◽  
Atmospheric Scattering ◽  
Adversarial Network ◽  
Two Parameters ◽  
Atmospheric Scattering Model ◽  
Single Image Dehazing

Single image dehazing has been a classic topic in computer vision for years. Motivated by the atmospheric scattering model, the key to satisfactory single image dehazing relies on an estimation of two physical parameters, i.e., the global atmospheric light and the transmission coefficient. Most existing methods employ a two-step pipeline to estimate these two parameters with heuristics which accumulate errors and compromise dehazing quality. Inspired by differentiable programming, we re-formulate the atmospheric scattering model into a novel generative adversarial network (DehazeGAN). Such a reformulation and adversarial learning allow the two parameters to be learned simultaneously and automatically from data by optimizing the final dehazing performance so that clean images with faithful color and structures are directly produced. Moreover, our reformulation also greatly improves the GAN’s interpretability and quality for single image dehazing. To the best of our knowledge, our method is one of the first works to explore the connection among generative adversarial models, image dehazing, and differentiable programming, which advance the theories and application of these areas. Extensive experiments on synthetic and realistic data show that our method outperforms state-of-the-art methods in terms of PSNR, SSIM, and subjective visual quality.

Single Image Dehazing by Predicting Atmospheric Scattering Parameters

2020 ◽  
Vol 2020 (1) ◽  
pp. 74-77
Author(s):  
Simone Bianco ◽  
Luigi Celona ◽  
Flavio Piccoli
Keyword(s):  
Reference Image ◽  
Single Image ◽  
Scattering Parameters ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Local Structures ◽  
Full Reference ◽  
Reconstruction Quality ◽  
Single Image Dehazing

In this work we propose a method for single image dehazing that exploits a physical model to recover the haze-free image by estimating the atmospheric scattering parameters. Cycle consistency is used to further improve the reconstruction quality of local structures and objects in the scene as well. Experimental results on four real and synthetic hazy image datasets show the effectiveness of the proposed method in terms of two commonly used full-reference image quality metrics.

Single image dehazing using a new color channel

2021 ◽  
Vol 74 ◽  
pp. 103008
Author(s):  
Geet Sahu ◽  
Ayan Seal ◽  
Ondrej Krejcar ◽  
Anis Yazidi
Keyword(s):  
Single Image ◽  
Color Channel ◽  
Image Dehazing ◽  
Single Image Dehazing

Deep Retinex Network for Single Image Dehazing

2021 ◽  
Vol 30 ◽  
pp. 1100-1115
Author(s):  
Pengyue Li ◽  
Jiandong Tian ◽  
Yandong Tang ◽  
Guolin Wang ◽  
Chengdong Wu
Keyword(s):  
Single Image ◽  
Image Dehazing ◽  
Single Image Dehazing
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