Abstract
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.