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

•We present a comprehensive review and new perspectives for data-driven single image deraining (SID);•We re-examine the three factors (i.e., data, rain model and network architecture) for SID problem;•We analyze the factors by proposing new and more reasonable criteria (i.e., general vs. specific, s...

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Published in:	Pattern recognition Vol. 143; p. 109740
Main Authors:	Zhang, Zhao, Wei, Yanyan, Zhang, Haijun, Yang, Yi, Yan, Shuicheng, Wang, Meng
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.11.2023
Subjects:	Comprehensive review Data Data-driven single image deraining Effectiveness of data In-depth analysis Network architecture New perspective of data Rain model Solving paradigms Comprehensive review Solving paradigms In-depth analysis Rain model Data-driven single image deraining Network architecture Data New perspective of data Effectiveness of data
ISSN:	0031-3203
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Abstract	•We present a comprehensive review and new perspectives for data-driven single image deraining (SID);•We re-examine the three factors (i.e., data, rain model and network architecture) for SID problem;•We analyze the factors 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 new perspectives of data, and reveal two different solving paradigms (explicit vs. implicit) for the SID task;•We profoundly studied one of the three factors, i.e., data, and measured the performance of current methods on different datasets by extensive experiments to reveal the effectiveness of SID data. 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.
AbstractList	•We present a comprehensive review and new perspectives for data-driven single image deraining (SID);•We re-examine the three factors (i.e., data, rain model and network architecture) for SID problem;•We analyze the factors 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 new perspectives of data, and reveal two different solving paradigms (explicit vs. implicit) for the SID task;•We profoundly studied one of the three factors, i.e., data, and measured the performance of current methods on different datasets by extensive experiments to reveal the effectiveness of SID data. 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.
ArticleNumber	109740
Author	Yan, Shuicheng Zhang, Haijun Wang, Meng Zhang, Zhao Wei, Yanyan Yang, Yi
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Keywords	Comprehensive review Solving paradigms In-depth analysis Rain model Data-driven single image deraining Network architecture Data New perspective of data Effectiveness of data
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Snippet	•We present a comprehensive review and new perspectives for data-driven single image deraining (SID);•We re-examine the three factors (i.e., data, rain model...
SourceID	crossref elsevier
SourceType	Enrichment Source Index Database Publisher
StartPage	109740
SubjectTerms	Comprehensive review Data Data-driven single image deraining Effectiveness of data In-depth analysis Network architecture New perspective of data Rain model Solving paradigms
Title	Data-Driven single image deraining: A Comprehensive review and new perspectives
URI	https://dx.doi.org/10.1016/j.patcog.2023.109740
Volume	143
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