Two-Phase Transmission Map Estimation for Robust Image Dehazing

Shu, Qiaoling; Wu, Chuansheng; Liu, Ryan Wen; Chui, Kwok Tai; Xiong, Shengwu

doi:10.1007/978-3-030-04224-0_46

Cited by 5 publications

(4 citation statements)

References 36 publications

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“…In contrast, the prior-based method mainly analyzes haze formation and relies on specific prior information to achieve image dehazing. Dark channel prior (DCP) [14] and its improvements [15][16][17] have an excellent performance in the image dehazing task. rough numerous statistics on outdoor haze-free images, He et al proposed DCP based on the assumption that most local color blocks contain some pixels with very low intensity in at least one color channel.…”

Section: Physical Model-based Methodsmentioning

confidence: 99%

“…To reduce halo and unnatural artifacts, a low-complexity color ellipsoid prior [20] is designed to accurately and swiftly estimate the transmission map. In current literature, several variational model-based transmission estimation methods [15,21,22] are also proposed. Although prior-based methods have verified excellent dehazing performance, they may cause a loss in color fidelity under certain circumstances and fail to obtain pleasing visual effects on maritime images.…”

Section: Physical Model-based Methodsmentioning

confidence: 99%

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CNN-Enabled Visibility Enhancement Framework for Vessel Detection under Haze Environment

Guo

Liang

2021

Journal of Advanced Transportation

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Maritime images captured under haze environment often have a terrible visual effect, making it easy to overlook important information. To avoid the failure of vessel detection caused by fog, it is necessary to preprocess the collected hazy images for recovering vital information. In this paper, a novel CNN-enabled visibility dehazing framework is proposed, consisting of two subnetworks, that is, Coarse Feature Extraction Module (C-FEM) and Fine Feature Fusion Module (F-FFM). Specifically, C-FEM is a multiscale haze feature extraction network, which can learn information from three scales. Correspondingly, F-FFM is an improved encoder-decoder network to fuse multiscale information obtained by C-FEM and enhance the visual effect of the final output. Meanwhile, a hybrid loss function is designed for monitoring the multiscale output of C-FEM and the final result of F-FFM simultaneously. It is worth mentioning that massive maritime images are considered the training dataset to further adapt the vessel detection task under haze environment. Comprehensive experiments on synthetic and realistic images have verified the superior effectiveness and robustness of our CNN-enabled visibility dehazing framework compared to several state-of-the-art methods. Our method preprocesses images before vessel detection to demonstrate our framework has the capacity of promoting maritime video surveillance.

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Section: Physical Model-based Methodsmentioning

confidence: 99%

Section: Physical Model-based Methodsmentioning

confidence: 99%

CNN-Enabled Visibility Enhancement Framework for Vessel Detection under Haze Environment

Guo

Liang

2021

Journal of Advanced Transportation

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“…e algorithm's defect lies in that the estimation of the scene depth of hazy white images is biased, affecting the dehazing effect. Subsequently, many variationsbased image dehazing methods have been proposed [7][8][9][10]. Although these methods can perform well in some situations, they cannot robustly process maritime images due to huge texture structure differences.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Enabled Variational Optimization Method for Image Dehazing in Maritime Intelligent Transportation Systems

Wang

Zhang

et al. 2021

Journal of Advanced Transportation

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Image dehazing has become a fundamental problem of common concern in computer vision-driven maritime intelligent transportation systems (ITS). The purpose of image dehazing is to reconstruct the latent haze-free image from its observed hazy version. It is well known that the accurate estimation of transmission map plays a vital role in image dehazing. In this work, the coarse transmission map is firstly estimated using a robust fusion-based strategy. A unified optimization framework is then proposed to estimate the refined transmission map and latent sharp image simultaneously. The resulting constrained minimization model is solved using a two-step optimization algorithm. To further enhance dehazing performance, the solutions of subproblems obtained in this optimization algorithm are equivalent to deep learning-based image denoising. Due to the powerful representation ability, the proposed method can accurately and robustly estimate the transmission map and latent sharp image. Numerous experiments on both synthetic and realistic datasets have been performed to compare our method with several state-of-the-art dehazing methods. Dehazing results have demonstrated the proposed method’s superior imaging performance in terms of both quantitative and qualitative evaluations. The enhanced imaging quality is beneficial for practical applications in maritime ITS, for example, vessel detection, recognition, and tracking.

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“…It is well known that high-quality dehazing performance is strongly dependent on the accurate estimation of transmission map. Many efforts have been devoted to refine the DCP-based coarse transmission map, such as soft matting method [2], guided image filtering [7], total generalized variation (TGV) [8], image guided TGV [9], non-local total variation [10], and kernel regression model [11], etc. Simultaneous estimation of transmission map and haze-free image have also been performed to enhance image dehazing [12].…”

Section: Introductionmentioning

confidence: 99%

Variational Regularized Transmission Refinement for Image Dehazing

Shu

Xiao

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

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High-quality dehazing performance is highly dependent upon the accurate estimation of transmission map. In this work, the coarse estimation version is first obtained by weightedly fusing two different transmission maps, which are generated from foreground and sky regions, respectively. A hybrid variational model with promoted regularization terms is then proposed to assisting in refining transmission map. The resulting complicated optimization problem is effectively solved via an alternating direction algorithm. The final haze-free image can be effectively obtained according to the refined transmission map and atmospheric scattering model. Our dehazing framework has the capacity of preserving important image details while suppressing undesirable artifacts, even for hazy images with large sky regions. Experiments on both synthetic and realistic images have illustrated that the proposed method is competitive with or even outperforms the state-of-the-art dehazing techniques under different imaging conditions.

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Two-Phase Transmission Map Estimation for Robust Image Dehazing

Cited by 5 publications

References 36 publications

CNN-Enabled Visibility Enhancement Framework for Vessel Detection under Haze Environment

CNN-Enabled Visibility Enhancement Framework for Vessel Detection under Haze Environment

Deep Learning-Enabled Variational Optimization Method for Image Dehazing in Maritime Intelligent Transportation Systems

Variational Regularized Transmission Refinement for Image Dehazing

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