Single Image Deraining: From Model-Based to Data-Driven and Beyond
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Rain removal or deraining methods attempt to restore the clean background scenes from images degraded by rain streaks and rain accumulation (or rain veiling effect). The early single-image deraining methods employ optimization methods on a cost function, where various priors are developed to represent the properties of rain and background-scene layers. Since 2017, single-image deraining methods step into a deep-learning era. They are built on deep-learning networks, i.e. convolutional neural networks, recurrent neural networks, generative adversarial networks, etc., and demonstrate impressive performance. Given the current rapid development, this article provides a comprehensive survey of deraining methods over the last decade. The rain appearance models are first summarized, and then followed by the discussion on 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 several ideas on deep learning, i.e., models, architectures, priors, auxiliary variables, loss functions, and training datasets. This survey presents milestones in cuttingedge single-image deraining methods, reviews a broad selection of previous works in different categories, and provides insights on the historical development route from the model-based to data-driven methods. It also summarizes performance comparisons quantitatively and qualitatively. Beyond discussing existing deraining methods, we also discuss future directions and trends.
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