A multi-scale fusion scheme based on haze-relevant features for single image dehazing

Li, Yunan; Miao, Qiguang; Liu, Ruyi; Song, Jianfeng; Quan, Yining; Huang, Yuhui

doi:10.1016/j.neucom.2017.12.046

Cited by 38 publications

(14 citation statements)

References 21 publications

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“…in the image is damaged, and the saliency of the target is also greatly affected. So the luminance component I , the saliency component S , and the saturation component I have a strong relationship with the distribution of haze [15,26,27]. The concentration of haze F(x, y) is described as follows:…”

Section: Transmission Estimationmentioning

confidence: 99%

“…Liu et al [14] developed a polarization dehazing method by processing the low spatial frequency parts and the high spatial frequency parts separately. Regarding the image fusion method, Li et al [15] proposed a single image dehazing approach based on a multiscale pyramid fusion scheme. In addition, with the development of deep learning, some researchers employed convolutional neural networks for single hazy image processing.…”

Section: Introductionmentioning

confidence: 99%

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Single Remote Sensing Multispectral Image Dehazing Based on a Learning Framework

Shao

Guo

Zhang

et al. 2019

Mathematical Problems in Engineering

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Given that a single remote sensing image dehazing is an ill-posed problem, this is still a challenging task. In order to improve the visibility of a single hazy remote sensing multispectral image, we developed a novel and effective algorithm based on a learning framework. A linear regression model with the relevant features of haze was established. And the gradient descent method is applied to the learning model. Then a hazy image accurate transmission map is obtained by learning the coefficients of the linear model. In addition, we proposed a more effective method to estimate the atmospheric light, which can restrain the influence of highlight areas on the atmospheric light acquisition. Compared with the traditional haze removal methods, the experimental results demonstrate that the proposed algorithm can achieve better visual effect and color fidelity. Both subjective evaluation and objective assessments indicate that the proposed method achieves a better performance than the state-of-the-art methods.

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Section: Transmission Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Single Remote Sensing Multispectral Image Dehazing Based on a Learning Framework

Shao

Guo

Zhang

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…For that reason, the results of enhancement methods are less natural. In addition, another line of work [28, 32] dealt with image dehazing by fusing multiple enhanced images together.…”

Section: Related Workmentioning

confidence: 99%

Learning mean progressive scattering using binomial truncated loss for image dehazing

Qiu

Liang

et al. 2019

IET image process

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In this study, the authors propose a novel progressive dehazing network to address the single image haze removal problem based on a new mean progressive scattering model. Different from methods that learn atmosphere light and transmission maps with different networks, these two variables are optimised in a unified network. Following the methodology of traditional prior‐based methods that estimate a coarse transmission map first, a progressive refinement branch in the decoder has been designed to restore the fine‐scale transmission map. To improve the prediction accuracy of the transmission map, a novel binomial truncated loss that assigns weights to error values according to the probabilities of error occurrences has been proposed. An ablation study is conducted to verify the effectiveness of the components in the proposed method. Experiments in the synthetic datasets and real images demonstrate that the proposed method outperforms other state‐of‐the‐art methods.

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“…When other images are used for substitution processing, the effect will be different. To improve the generalization ability or robustness of an algorithm, some scholars proposed that different methods or features are fused to achieve the haze removal [9,14,[36][37][38][39]. Using fusion strategy to process images, the key technology is how to determine the fusion rule, because different rules will produce different results.…”

Section: Introductionmentioning

confidence: 99%

A New Haze Removal Algorithm for Single Urban Remote Sensing Image

et al. 2020

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The remote sensing imaging detection technology is an important means to effectively monitor and manage urban environment and resources, and remote sensing images are an important data source of smart city and digital city. The existence of haze has a serious impact on the quality of optical remote sensing image acquisition, resulting in remote sensing image blurred, detail information loss, contrast decreased and color distortion. To reduce the impact of haze and give full play to the value of remote sensing images, a new urban remote sensing haze removal (URSHR) algorithm is proposed in this paper, which combines the image phase consistency feature, multi-scale Retinax theory and histogram characteristic. In URSHR method, firstly the image haze is removed by using multi-scale Retinex theory and histogram characteristic, and then the detail information of the image is enhanced by using the phase consistency features, finally they are fused with the multi-scale wavelet transform. It achieves the purpose of both removing haze and enhancing geometric detail information. The many verification experiments were carried out by using real urban remote sensing image data, and good results were obtained. This shows that the new algorithm is a feasible and effective for urban remote sensing image haze removal, and it has good application and promotion value.

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A multi-scale fusion scheme based on haze-relevant features for single image dehazing

Cited by 38 publications

References 21 publications

Single Remote Sensing Multispectral Image Dehazing Based on a Learning Framework

Single Remote Sensing Multispectral Image Dehazing Based on a Learning Framework

Learning mean progressive scattering using binomial truncated loss for image dehazing

A New Haze Removal Algorithm for Single Urban Remote Sensing Image

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