Rain-Free and Residue Hand-in-Hand: A Progressive Coupled Network for Real-Time Image Deraining

Rainy weather is a challenge for many vision-oriented tasks ( e.g. , object detection and segmentation), which causes performance degradation. Image deraining is an effective solution to avoid performance drop of downstream vision tasks. However, most existing deraining methods either fail to produc...

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Veröffentlicht in:IEEE transactions on image processing Jg. 30; S. 7404 - 7418
Hauptverfasser: Jiang, Kui, Wang, Zhongyuan, Yi, Peng, Chen, Chen, Wang, Zheng, Wang, Xiao, Jiang, Junjun, Lin, Chia-Wen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
attention mechanism
Blending
Computational modeling
Customer relationship management
Degradation
Feature extraction
Image degradation
Image deraining
Image enhancement
Image restoration
Image segmentation
multi-scale fusion
non-local network
Object recognition
Performance degradation
Production methods
Rain
Source code
Task analysis
Vision
ISSN:1057-7149, 1941-0042, 1941-0042
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Zusammenfassung:Rainy weather is a challenge for many vision-oriented tasks ( e.g. , object detection and segmentation), which causes performance degradation. Image deraining is an effective solution to avoid performance drop of downstream vision tasks. However, most existing deraining methods either fail to produce satisfactory restoration results or cost too much computation. In this work, considering both effectiveness and efficiency of image deraining, we propose a progressive coupled network (PCNet) to well separate rain streaks while preserving rain-free details. To this end, we investigate the blending correlations between them and particularly devise a novel coupled representation module (CRM) to learn the joint features and the blending correlations. By cascading multiple CRMs, PCNet extracts the hierarchical features of multi-scale rain streaks, and separates the rain-free content and rain streaks progressively. To promote computation efficiency, we employ depth-wise separable convolutions and a U-shaped structure, and construct CRM in an asymmetric architecture to reduce model parameters and memory footprint. Extensive experiments are conducted to evaluate the efficacy of the proposed PCNet in two aspects: (1) image deraining on several synthetic and real-world rain datasets and (2) joint image deraining and downstream vision tasks ( e.g. , object detection and segmentation). Furthermore, we show that the proposed CRM can be easily adopted to similar image restoration tasks including image dehazing and low-light enhancement with competitive performance. The source code is available at https://github.com/kuijiang0802/PCNet .
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2021.3102504