A Coarse-to-Fine Multi-stream Hybrid Deraining Network for Single Image Deraining

Wei, Yanyan; Zhang, Zhao; Zhang, Haijun; Hong, Richang; Wang, Meng

doi:10.1109/icdm.2019.00073

Cited by 40 publications

(25 citation statements)

References 42 publications

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“…Different from these methods, the proposed GTA-Net abandons the time-consuming alignment operation and designs two submodules to extract temporal information in a coarse-to-fine manner, which can not only utilize temporal information effectively but also remove rain streaks in real time. To the best of our knowledge, our work is the first work to consider executing the coarse-to-fine mechanism in temporal aggregation, which is different from previous related works [6,13].…”

Section: Related Workmentioning

confidence: 88%

GTA-Net: Gradual Temporal Aggregation Network for Fast Video Deraining

Xue

Meng

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Recently, the development of intelligent technology arouses the requirements of high-quality videos. Rain streak is a frequent and inevitable factor to degrade the video. Many researchers have put their energies into eliminating the adverse effects of rainy video. Unfortunately, how to fully utilize the temporal information from rainy video is still in suspense. In this work, to effectively exploit temporal information, we develop a simple but effective network, Gradual Temporal Aggregation Network (GTA-Net for short). To be specific, according to the temporal distance between rainy frames and the reference frame, we divide the rainy frames into different groups. A multi-stream coarse temporal aggregation module is first performed to aggregate different temporal information with equal status and importance. Then we design a singlestream fine temporal aggregation module to further fuse the integrated frames that maintain the different distances with the target frame. In this way of coarse-to-fine, we not only achieve superior performance, but also gain the surprising execution speed owing to abandon the time-consuming alignment operation. Plenty of experimental results demonstrate that our GTA-Net performs favorably compared to other stateof-the-art approaches. The meticulous ablation study further indicates the effectiveness of our designed GTA-Net.

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Section: Related Workmentioning

confidence: 88%

GTA-Net: Gradual Temporal Aggregation Network for Fast Video Deraining

Xue

Meng

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…A novel density-aware multi-stream dense convolutional network-based framework [6] was proposed to jointly estimate the rain density and deraining. More recently, a hybrid block [7] has been proposed to extract the rain streak more precisely, especially in heavy rain condition. Similarly, a better and simpler baseline deraining network is proposed in [8] by repeatedly unfolding a shallow ResNet to take advantage of the recursive computation.…”

Section: Related Workmentioning

confidence: 99%

“…Because Eqn. (1) is an ill-posed problem, some feasible approaches have been proposed to solve it, include both the traditional [1,2,3] and deep learning-based models [4,5,6,7,8,9]. It is noteworthy that most existing deep deraining networks are supervised methods using paired information in * denotes the corresponding author, e-mails: cszzhang@gmail.com.…”

Section: Introductionmentioning

confidence: 99%

Semi-Deraingan: A New Semi-Supervised Single Image Deraining

Wei

Zhang

Wang

et al. 2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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Although supervised single image deraining (SID) have obtained impressive results, they still cannot obtain satisfactory results on real images for the weak generalization of rain removal capacity. In this paper, we mainly discuss the semisupervised SID and propose a new GAN-based deraining network called Semi-DerainGAN, which can use both synthetic and real data in a uniform network based on two supervised and unsupervised processes. For this task, a semi-supervised rain streak learner termed SSRML sharing the same parameters of both processes is derived, which makes the real images contribute more rain streak information, so that the resulted model has a strong generalization power to the real SID task. We also contribute a new real-world rain image dataset called Real200 to alleviate the difference between both synthetic and real image domains. Extensive results on public datasets show that our model can obtain competitive results, especially on the real rain images.

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“…MPRNet [50] CCN [34] Semi-DerainGAN [58] CVID [59] RCDNet [53] Pan et al [60] MSPFN [44] RDDAN [61] JDNet [45] QuDeC [62] Syn2Real [ [70] GraNet [46] LPNet [71] PReNet [31] DAF-Net [22] ReHEN [72] DDC-Net [35] RR-GAN [73] MH-DerainNet [74] SIRR [75] SPANet [33] ReMAEN [76] UD-GAN [77] UMRL [78] ID-CGAN [32] Li et al [23] JORDER-E [56] JORDER [21] RWL [79] DualCNN [80] NLEDN [81] RESCAN [55] ResGuideNet [82] Qian et al [25] DID-MDN [24] Li et al [83] DerainNet [83] Fu et al [20] Quan et al [52] SSDRNet [67] MOEDN [56] RICNet [35] JRGR [57] RLNet [59] QSMD [58] Fig. 7: The division of recent SID methods from six aspects based on the three factors.…”

Section: Synthetical Mathematical General Specificmentioning

confidence: 99%

Data-Driven Single Image Deraining: A Comprehensive Review and New Perspectives

Zhang¹,

Yue²,

Zhang³

et al. 2021

Preprint

Self Cite

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<div>Abstract—Due to the powerful fitting ability of neural networks and massive training data, data-driven Single Image Deraining (SID) methods have obtained significant performance, and most of the existing studies focus on improving the deraining performance by proposing different kinds of deraining networks. However, the generalization ability of current SID methods may still be limited in the real scenario, and the deraining results also cannot effectively improve the performance of subsequent high-level tasks (such as object detection). The main reason may be because the current works mainly focus on designing new deraining neural networks, while neglecting the interpretation of the solving process. To investigate these issues, we in this paper re-examine the three important factors (i.e.,data, rain model and network architecture) for the SID task, and specifically analyze them by proposing new and more reasonable criteria (i.e., general vs. specific, synthetical vs. mathematical, black-box vs. white-box). We will also study the relationship of the three factors from new perspectives of data, and reveal two different solving paradigms (explicit vs. implicit) for the SID task. Besides, we also profoundly studied one of the three factors, i.e., data, and measured the performance of current methods on different datasets via extensive experiments to reveal the effectiveness of SID data. Finally, with the above comprehensive review and in-depth analysis, we also draw some valuable conclusions and suggestions for future research.</div>

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A Coarse-to-Fine Multi-stream Hybrid Deraining Network for Single Image Deraining

Cited by 40 publications

References 42 publications

GTA-Net: Gradual Temporal Aggregation Network for Fast Video Deraining

GTA-Net: Gradual Temporal Aggregation Network for Fast Video Deraining

Semi-Deraingan: A New Semi-Supervised Single Image Deraining

Data-Driven Single Image Deraining: A Comprehensive Review and New Perspectives

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