2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDTEC-3.6
Paper Title PCNET: PROGRESSIVE COUPLED NETWORK FOR REAL-TIME IMAGE DERAINING
Authors Kui Jiang, Zhongyuan Wang, Peng Yi, Wuhan University, China; Chen Chen, University of Central Florida, United States; Zheng Wang, Wuhan University, China; Chia-Wen Lin, National Tsing Hua University, Taiwan
SessionTEC-3: Restoration and Enhancement 3
LocationArea G
Session Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Poster
Topic Image and Video Processing: Restoration and enhancement
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Image deraining is an effective solution to avoid performance drop of vision-oriented tasks in rainy weather. Most existing image deraining approaches either fail to produce satisfactory restoration results or cost too much computation. In this pa-per, we propose a low-complexity and high-performance coupled representation module (CRM), designed to learn the joint features of rain-free contents and rain information as well as their blending correlations. To promote the computation efficiency, we employ depth-wise separable convolutions, and construct CRM in an asymmetric U-shaped architecture to reduce model parameters and memory footprint. Our final model – PCNet achieves the progressive separation of rain-free contents and rain streaks using cascaded residual learning. Extensive experiments are conducted to evaluate the efficacy of the proposed PCNet on several synthetic and real-world rain datasets.