Natural Image Dehazing Based on L 0 Gradient Minimization

2015 ◽  
pp. 603-610
Author(s):  
Jingjing Qi ◽  
Wen Lu ◽  
Shuyu Yang ◽  
Xinbo Gao
Keyword(s):  
Natural Image ◽  
Image Dehazing

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.

Review and prospect of image dehazing techniques

2010 ◽  
Vol 30 (9) ◽  
pp. 2417-2421 ◽  
Author(s):  
Fan GUO ◽  
Zi-xing CAI ◽  
Bin XIE ◽  
Jin TANG
Keyword(s):  
Image Dehazing

An Image Dehazing Algorithm Based on Improved Atmospheric Scattering Model

2019 ◽  
Vol 31 (7) ◽  
pp. 1148 ◽  
Author(s):  
Xinnan Fan ◽  
Shuyue Ye ◽  
Pengfei Shi ◽  
Xuewu Zhang ◽  
Jinxiang Ma
Keyword(s):  
Image Dehazing ◽  
Scattering Model ◽  
Atmospheric Scattering ◽  
Atmospheric Scattering Model

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

scholarly journals Improved SinGAN Integrated with an Attentional Mechanism for Remote Sensing Image Classification

2021 ◽  
Vol 13 (9) ◽  
pp. 1713
Author(s):  
Songwei Gu ◽  
Rui Zhang ◽  
Hongxia Luo ◽  
Mengyao Li ◽  
Huamei Feng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Life ◽  
Attention Mechanism ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Natural Image ◽  
Remote Sensing Images ◽  
Training Time ◽  
Adversarial Networks ◽  
Remote Sensing Image Classification

Deep learning is an important research method in the remote sensing field. However, samples of remote sensing images are relatively few in real life, and those with markers are scarce. Many neural networks represented by Generative Adversarial Networks (GANs) can learn from real samples to generate pseudosamples, rather than traditional methods that often require more time and man-power to obtain samples. However, the generated pseudosamples often have poor realism and cannot be reliably used as the basis for various analyses and applications in the field of remote sensing. To address the abovementioned problems, a pseudolabeled sample generation method is proposed in this work and applied to scene classification of remote sensing images. The improved unconditional generative model that can be learned from a single natural image (Improved SinGAN) with an attention mechanism can effectively generate enough pseudolabeled samples from a single remote sensing scene image sample. Pseudosamples generated by the improved SinGAN model have stronger realism and relatively less training time, and the extracted features are easily recognized in the classification network. The improved SinGAN can better identify sub-jects from images with complex ground scenes compared with the original network. This mechanism solves the problem of geographic errors of generated pseudosamples. This study incorporated the generated pseudosamples into training data for the classification experiment. The result showed that the SinGAN model with the integration of the attention mechanism can better guarantee feature extraction of the training data. Thus, the quality of the generated samples is improved and the classification accuracy and stability of the classification network are also enhanced.

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

scholarly journals An Underwater Image Enhancement Algorithm Based on Generative Adversarial Network and Natural Image Quality Evaluation Index

2021 ◽  
Vol 9 (7) ◽  
pp. 691
Author(s):  
Kai Hu ◽  
Yanwen Zhang ◽  
Chenghang Weng ◽  
Pengsheng Wang ◽  
Zhiliang Deng ◽  
...  
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Quality Evaluation ◽  
High Efficiency ◽  
Natural Image ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Image Quality Evaluation ◽  
Adversarial Network ◽  
Underwater Image

When underwater vehicles work, underwater images are often absorbed by light and scattered and diffused by floating objects, which leads to the degradation of underwater images. The generative adversarial network (GAN) is widely used in underwater image enhancement tasks because it can complete image-style conversions with high efficiency and high quality. Although the GAN converts low-quality underwater images into high-quality underwater images (truth images), the dataset of truth images also affects high-quality underwater images. However, an underwater truth image lacks underwater image enhancement, which leads to a poor effect of the generated image. Thus, this paper proposes to add the natural image quality evaluation (NIQE) index to the GAN to provide generated images with higher contrast and make them more in line with the perception of the human eye, and at the same time, grant generated images a better effect than the truth images set by the existing dataset. In this paper, several groups of experiments are compared, and through the subjective evaluation and objective evaluation indicators, it is verified that the enhanced image of this algorithm is better than the truth image set by the existing dataset.

Single nighttime image dehazing based on image decomposition

2021 ◽  
Vol 183 ◽  
pp. 107986
Author(s):  
Yun Liu ◽  
Anzhi Wang ◽  
Hao Zhou ◽  
Pengfei Jia
Keyword(s):  
Image Decomposition ◽  
Image Dehazing ◽  
Nighttime Image

Natural image understanding using algorithm selection and high-level feedback

2013 ◽  
Author(s):  
Martin Lukac ◽  
Michitaka Kameyama ◽  
Kosuke Hiura
Keyword(s):  
Image Understanding ◽  
Natural Image ◽  
Algorithm Selection ◽  
High Level
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