Learning to Restore Hazy Video: A New Real-World Dataset and A New Method

Zhang, Xinyi; Dong, Hang; Pan, Jinshan; Zhu, Chao; Tai, Ying; Wang, Chengjie; Li, Jilin; Huang, Feiyue; Wang, Fei

doi:10.1109/cvpr46437.2021.00912

Cited by 48 publications

(13 citation statements)

References 27 publications

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“…Histogram of oriented gradient (HOG) and autocorrelation loss are used to facilitate the orientation consistency and repress repetitive rain streaks. They trained the network all the way from drizzle to downpour rain Fusion [110] LiDAR [152] LiDAR [76] LiDAR [153] Others [154] LiDAR [155] LiDAR [156] Camera [157] Camera [158] Camera [159] Camera [160] Camera [161] Camera [162] Camera [163] Camera [164] LiDAR [165] LiDAR [166] LiDAR [128] LiDAR [29] Fusion [129] LiDAR [167] Fusion [168] LiDAR [169] LiDAR [170] Fusion [171] LiDAR [172] Camera [173] Camera [174] Camera [175] Camera [176] Camera [177] Camera [178] Camera [179] Camera [180] Camera [181] Camera [182] Camera [183] Camera [184] Camera [185] Camera [186] Fusion [187] Fusion [188] LiDAR [189] Camera [190] Camera…”

Section: Rainmentioning

confidence: 99%

“…CR ensures that the restored image is pulled closer to the clear image and pushed away from the hazy image in the representation space. Zhang et al [175] employ temporal redundancy from neighborhood hazy frames to perform video de-hazing. Authors collect a real-world video de-hazing dataset containing pairs of real hazy and corresponding haze-free videos.…”

Section: Fogmentioning

confidence: 99%

See 1 more Smart Citation

Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

Zhang¹,

Carballo²,

Yang³

et al. 2021

Preprint

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Automated Driving Systems (ADS) open up a new domain for the automotive industry and offer new possibilities for future transportation with higher efficiency and comfortable experiences. However, autonomous driving under adverse weather conditions has been the problem that keeps autonomous vehicles (AVs) from going to level 4 or higher autonomy for a long time. This paper assesses the influences and challenges that weather brings to ADS sensors in an analytic and statistical way, and surveys the solutions against inclement weather conditions. Stateof-the-art techniques on perception enhancement with regard to each kind of weather are thoroughly reported. External auxiliary solutions like V2X technology, weather conditions coverage in currently available datasets, simulators, and experimental facilities with weather chambers are distinctly sorted out. By pointing out all kinds of major weather problems the autonomous driving field is currently facing, and reviewing both hardware and computer science solutions in recent years, this survey contributes a holistic overview on the obstacles and directions of ADS development in terms of adverse weather driving conditions.

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Section: Rainmentioning

confidence: 99%

Section: Fogmentioning

confidence: 99%

Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

Zhang¹,

Carballo²,

Yang³

et al. 2021

Preprint

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show abstract

“…To train the proposed video dehazing network, we select a real haze video dataset: the REVIDE dataset [34]…”

Section: A Datasetsmentioning

confidence: 99%

Progressive Deep Video Dehazing without Explicit Alignment Estimation

Li¹

2021

Preprint

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To solve the issue of video dehazing, there are two main tasks to attain: how to align adjacent frames to the reference frame; how to restore the reference frame. Some papers adopt explicit approaches (e.g., the Markov random field, optical flow, deformable convolution, 3D convolution) to align neighboring frames with the reference frame in feature space or image space, they then use various restoration methods to achieve the final dehazing results. In this paper, we propose a progressive alignment and restoration method for video dehazing. The alignment process aligns consecutive neighboring frames stage by stage without using the optical flow estimation. The restoration process is not only implemented under the alignment process but also uses a refinement network to improve the dehazing performance of the whole network. The proposed networks include four fusion networks and one refinement network. To decrease the parameters of networks, three fusion networks in the first fusion stage share the same parameters. Extensive experiments demonstrate that the proposed video dehazing method achieves outstanding performance against the-state-of-art methods.

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“…The current methods for dehazing mainly include the data-driven method [1][2][3], the method based on prior knowledge [4][5][6], and the method based on physical models [7][8][9][10]. The first two types of methods hardly contain physical models, therefore, the problem these methods solved is essentially ill-posed.…”

Section: Introductionmentioning

confidence: 99%

Polarization-Based Haze Removal Using Self-Supervised Network

et al. 2022

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Atmospheric scattering caused by suspended particles in the air severely degrades the scene radiance. This paper proposes a method to remove haze by using a neural network that combines scene polarization information. The neural network is self-supervised and online globally optimization can be achieved by using the atmospheric transmission model and gradient descent. Therefore, the proposed method does not require any haze-free image as the constraint for neural network training. The proposed approach is far superior to supervised algorithms in the performance of dehazing and is highly robust to the scene. It is proved that this method can significantly improve the contrast of the original image, and the detailed information of the scene can be effectively enhanced.

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Learning to Restore Hazy Video: A New Real-World Dataset and A New Method

Cited by 48 publications

References 27 publications

Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

Progressive Deep Video Dehazing without Explicit Alignment Estimation

Polarization-Based Haze Removal Using Self-Supervised Network

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