Data-Driven single image deraining: A Comprehensive review and new perspectives

Zhang, Z; Wei, Y; Zhang, H; Yang, Y; Yan, S; Wang, M

Data-Driven single image deraining: A Comprehensive review and new perspectives

Zhang, Z Wei, Y Zhang, H Yang, Y

Yan, S Wang, M

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Pattern Recognition, 2023, 143, pp. 109740
Issue Date:: 2023-11-01

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Field	Value	Language
dc.contributor.author	Zhang, Z
dc.contributor.author	Wei, Y
dc.contributor.author	Zhang, H
dc.contributor.author	Yang, Y https://orcid.org/0000-0002-0512-880X
dc.contributor.author	Yan, S
dc.contributor.author	Wang, M
dc.date.accessioned	2024-04-20T20:34:10Z
dc.date.available	2024-04-20T20:34:10Z
dc.date.issued	2023-11-01
dc.identifier.citation	Pattern Recognition, 2023, 143, pp. 109740
dc.identifier.issn	0031-3203
dc.identifier.uri	http://hdl.handle.net/10453/178122
dc.description.abstract	Single Image Deraining (SID) aims at recovering the rain-free background from an image degraded by rain streaks. For the powerful fitting ability of deep neural networks and massive training data, data-driven deep SID methods have obtained significant improvement over traditional model/prior-based ones. Current studies usually focus on improving the deraining performance by proposing different categories of deraining networks, while neglecting the interpretation of the solving process. As a result, the generalization ability may still be limited in real-world scenarios, and the deraining results also cannot effectively improve the performance of subsequent high-level tasks (e.g., object detection). To explore these issues, we in this paper re-examine the three important factors (i.e., data, rain model and network architecture) for the SID problem, 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 also study the relationship of the three factors from a new perspective of data, and reveal two different solving paradigms (explicit vs. implicit) for the SID task. We further discuss the current mainstream data-driven SID methods from five aspects, i.e., training strategy, network pipeline, domain knowledge, data preprocessing, and objective function, and some useful conclusions are summarized by statistics. Besides, we profoundly studied one of the three factors, i.e., data, and measured the performance of current methods on different datasets through extensive experiments to reveal the effectiveness of SID data. Finally, with the comprehensive review and in-depth analysis, we draw some valuable conclusions and suggestions for future research.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Pattern Recognition
dc.relation.isbasedon	10.1016/j.patcog.2023.109740
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4603 Computer vision and multimedia computation
dc.subject.classification	4605 Data management and data science
dc.subject.classification	4611 Machine learning
dc.title	Data-Driven single image deraining: A Comprehensive review and new perspectives
dc.type	Journal Article
utslib.citation.volume	143
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-20T20:34:05Z
pubs.publication-status	Published
pubs.volume	143

Abstract:

Single Image Deraining (SID) aims at recovering the rain-free background from an image degraded by rain streaks. For the powerful fitting ability of deep neural networks and massive training data, data-driven deep SID methods have obtained significant improvement over traditional model/prior-based ones. Current studies usually focus on improving the deraining performance by proposing different categories of deraining networks, while neglecting the interpretation of the solving process. As a result, the generalization ability may still be limited in real-world scenarios, and the deraining results also cannot effectively improve the performance of subsequent high-level tasks (e.g., object detection). To explore these issues, we in this paper re-examine the three important factors (i.e., data, rain model and network architecture) for the SID problem, 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 also study the relationship of the three factors from a new perspective of data, and reveal two different solving paradigms (explicit vs. implicit) for the SID task. We further discuss the current mainstream data-driven SID methods from five aspects, i.e., training strategy, network pipeline, domain knowledge, data preprocessing, and objective function, and some useful conclusions are summarized by statistics. Besides, we profoundly studied one of the three factors, i.e., data, and measured the performance of current methods on different datasets through extensive experiments to reveal the effectiveness of SID data. Finally, with the comprehensive review and in-depth analysis, we draw some valuable conclusions and suggestions for future research.

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http://hdl.handle.net/10453/178122