RefineDNet: A Weakly Supervised Refinement Framework for Single Image Dehazing

Zhao, Shiyu; Zhang, Lin; Shen, Ying; Zhou, Yicong

doi:10.1109/tip.2021.3060873

Cited by 237 publications

(149 citation statements)

References 45 publications

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“…To ensure the restored image was similar to the clear images, and away from the position of hazy images, CR was employed. Method RefineDNet [35], which combines prior knowledge with deep learning, restores visibility through dark channel prior and adversarial learning are utilized for second stage processing.…”

Section: Related Workmentioning

confidence: 99%

Lightweight multiple scale-patch dehazing network for real-world hazy image

2021

KSII TIIS

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Image dehazing is an ill-posed problem which is far from being solved. Traditional image dehazing methods often yield mediocre effects and possess substandard processing speed, while modern deep learning methods perform best only in certain datasets. The haze removal effect when processed by said methods is unsatisfactory, meaning the generalization performance fails to meet the requirements. Concurrently, due to the limited processing speed, most dehazing algorithms cannot be employed in the industry. To alleviate said problems, a lightweight fast dehazing network based on a multiple scale-patch framework (MSP) is proposed in the present paper. Firstly, the multi-scale structure is employed as the backbone network and the multi-patch structure as the supplementary network. Dehazing through a single network causes problems, such as loss of object details and color in some image areas, the multipatch structure was employed for MSP as an information supplement. In the algorithm image processing module, the image is segmented up and down for processed separately. Secondly, MSP generates a clear dehazing effect and significant robustness when targeting real-world homogeneous and nonhomogeneous hazy maps and different datasets. Compared with existing dehazing methods, MSP demonstrated a fast inference speed and the feasibility of real-time processing. The overall size and model parameters of the entire dehazing model are 20.75M and 6.8M, and the processing time for the single image is 0.026s. Experiments on NTIRE 2018 and NTIRE 2020 demonstrate that MSP can achieve superior performance among the stateof-the-art methods, such as PSNR, SSIM, LPIPS, and individual subjective evaluation.

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Section: Related Workmentioning

confidence: 99%

Lightweight multiple scale-patch dehazing network for real-world hazy image

2021

KSII TIIS

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“…However, the proposed method yielded images where the over-bright areas tended to lose some final image features. A more recent study by Zhao et al [22] merged the merits of prior-based and learning-based approaches. The method [22] combines visibility restoration and realness improvement sub-tasks using two-staged weakly supervised dehazing network.…”

Section: Introductionmentioning

confidence: 99%

“…A more recent study by Zhao et al [22] merged the merits of prior-based and learning-based approaches. The method [22] combines visibility restoration and realness improvement sub-tasks using two-staged weakly supervised dehazing network. The results of the work had little washed-out effects despite having better performance than existing state-of-the art methods.…”

Section: Introductionmentioning

confidence: 99%

Image Dehazing Based on Pixel Guided CNN with PAM via Graph Cut

Alenezi¹

2022

Computers, Materials &Amp; Continua

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Image dehazing is still an open research topic that has been undergoing a lot of development, especially with the renewed interest in machine learning-based methods. A major challenge of the existing dehazing methods is the estimation of transmittance, which is the key element of haze-affected imaging models. Conventional methods are based on a set of assumptions that reduce the solution search space. However, the multiplication of these assumptions tends to restrict the solutions to particular cases that cannot account for the reality of the observed image. In this paper we reduce the number of simplified hypotheses in order to attain a more plausible and realistic solution by exploiting a priori knowledge of the ground truth in the proposed method. The proposed method relies on pixel information between the ground truth and haze image to reduce these assumptions. This is achieved by using ground truth and haze image to find the geometric-pixel information through a guided Convolution Neural Networks (CNNs) with a Parallax Attention Mechanism (PAM). It uses the differential pixel-based variance in order to estimate transmittance. The pixel variance uses local and global patches between the assumed ground truth and haze image to refine the transmission map. The transmission map is also improved based on improved Markov random field (MRF) energy functions. We used different images to test the proposed algorithm. The entropy value of the proposed method was 7.43 and 7.39, a percent increase of 4.35% and 5.42%, respectively, compared to the best existing results. The increment is similar in other performance quality metrics and this validate its superiority compared to other existing methods in terms of key image quality evaluation metrics. The proposed approach's drawback, an over-reliance on real ground truth images, is also investigated. The proposed method show more details hence yields better images than those from the existing state-of-the-art-methods.

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“…Especially for haze, floating particles in haze lead to the fading and blurring of pictures, and the reduction of contrast and softness. They absorb and scatter light, resulting in serious color attenuation, poor clarity and contrast, and poor visual effect, which has a serious impact on subsequent computer vision tasks [7][8][9][10][11][12][13][14][15][16]. Therefore, it is necessary to remove haze effectively.…”

Section: Introductionmentioning

confidence: 99%

GGADN: Guided generative adversarial dehazing network

2021

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Image dehazing has always been a challenging topic in image processing. The development of deep learning methods, especially the Generative Adversarial Networks(GAN), provides a new way for image dehazing. In recent years, many deep learning methods based on GAN have been applied to image dehazing. However, GAN has two problems in image dehazing. Firstly, For haze image, haze not only reduces the quality of the image, but also blurs the details of the image. For Gan network, it is difficult for the generator to restore the details of the whole image while removing the haze. Secondly, GAN model is defined as a minimax problem, which weakens the loss function. It is difficult to distinguish whether GAN is making progress in the training process. Therefore, we propose a Guided Generative Adversarial Dehazing Network(GGADN). Different from other generation adversarial networks, GGADN adds a guided module on the generator. The guided module verifies the network of each layer of the generator. At the same time, the details of the map generated by each layer are strengthened. Network training is based on the pre-trained VGG feature model and L1-regularized gradient prior which is developed by new loss function parameters. From the dehazing results of synthetic images and real images, proposed method is better than the state-of-the-art dehazing methods.

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RefineDNet: A Weakly Supervised Refinement Framework for Single Image Dehazing

Cited by 237 publications

References 45 publications

Lightweight multiple scale-patch dehazing network for real-world hazy image

Lightweight multiple scale-patch dehazing network for real-world hazy image

Image Dehazing Based on Pixel Guided CNN with PAM via Graph Cut

GGADN: Guided generative adversarial dehazing network

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