  The goal of single-image deraining is to restore the rain-free background
scenes of an image degraded by rain streaks and rain accumulation. The early
single-image deraining methods employ a cost function, where various priors are
developed to represent the properties of rain and background layers. Since
2017, single-image deraining methods step into a deep-learning era, and exploit
various types of networks, i.e. convolutional neural networks, recurrent neural
networks, generative adversarial networks, etc., demonstrating impressive
performance. Given the current rapid development, in this paper, we provide a
comprehensive survey of deraining methods over the last decade. We summarize
the rain appearance models, and discuss two categories of deraining approaches:
model-based and data-driven approaches. For the former, we organize the
literature based on their basic models and priors. For the latter, we discuss
developed ideas related to architectures, constraints, loss functions, and
training datasets. We present milestones of single-image deraining methods,
review a broad selection of previous works in different categories, and provide
insights on the historical development route from the model-based to
data-driven methods. We also summarize performance comparisons quantitatively
and qualitatively. Beyond discussing the technicality of deraining methods, we
also discuss the future directions.
