Please cite this work as Yanyan Wei, Zhao Zhang, Mingliang Xu, Richang Hong, Jicong Fan and Shuicheng Yan, “Robust Attention Deraining Network for Synchronous Rain Streaks and Raindrops Removal,” In: Proceedings of the 30th ACM International Conference on Multimedia (ACM MM), Lisbon, Portugal, June 2022.

Please cite this work as follows: Zhao Zhang, Huan Zheng, Richang Hong, Mingliang Xu, Shuicheng Yan and Meng Wang, “Deep Color Consistent Network for Low-Light Image Enhancement,” In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

Please cite this work as: Huan Zheng, Zhao Zhang, Haijun Zhang, Yi Yang, Shuicheng Yan and Meng Wang, “Deep Multi-Resolution Mutual Learning for Image Inpainting,” In: Proceedings of the 30th ACM International Conference on Multimedia (ACM MM), Lisbon, Portugal, June 2022.

Unsupervised blind motion deblurring is still a challenging topic due to the inherent ill-posed properties, and lacking of paired data and accurate quality assessment method. Besides, virtually all the current studies suffer from large chromatic aberration between the latent and original images, which will directly cause the loss of image details. However, how to model and quantify the chromatic aberration appropriately are difficult issues urgent to be solved. In this paper, we propose a general unsupervised color retention network termed CRNet for blind motion deblurring, which can be easily extended to other tasks suffering from chromatic aberration. New concepts of blur offset estimation and adaptive blur correction are introduced, so that more detailed information can be retained to improve the deblurring task. Specifically, CRNet firstly learns a mapping from the blurry image to motion offset, rather than directly from the blurry image to latent image as previous work. With obtained motion offset, an adaptive blur correction operation is then performed on the original blurry image to obtain the latent image, thereby retaining the color information of image to the greatest extent. A new pyramid global blur feature perception module is also designed to further retain the color information and extract more blur information. To assess the color retention ability for image deblurring, we present a new chromatic aberration quantization metric termed Color-Sensitive Error (CSE) in line with human perception, which can be applied to both the cases with/without paired data. Extensive experiments demonstrated the effectiveness of our CRNet for the color retention in unsupervised deblurring.

Single Image Deraining task aims at recovering the rain-free background from an image degraded by rain streaks and rain accumulation. For the powerful fitting ability of deep neural networks and massive training data, data-driven deep SID methods obtained significant improvement over traditional ones. Current SID methods usually focus on improving the deraining performance by proposing different kinds 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. Please cite this work as: Zhao Zhang, Yanyan Wei, Haijun Zhang, Yi Yang, Shuicheng Yan and Meng Wang, “Data-Driven Single Image Deraining: A Comprehensive Review and New Perspectives,” Pattern Recognition (PR), May 2023.  

A document by Zhao Zhang . Click on the document to view its contents.