Single Image Deraining: From Model-Based to Data-Driven and Beyond (1912.07150v2)

Abstract: 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.

• The paper provides a comprehensive survey reviewing single-image deraining methods, tracing the evolution from traditional model-based techniques to modern data-driven deep learning approaches.
• Data-driven deep learning methods show superior quantitative and qualitative performance but face challenges due to the domain gap between synthetic training data and complex real-world rain conditions.
• Future research directions include integrating physics-based models, developing better rain synthesis, improving evaluation metrics, and handling real-time and complex scenarios.

Overview of "Single Image Deraining: From Model-Based to Data-Driven and Beyond"

The paper "Single Image Deraining: From Model-Based to Data-Driven and Beyond" offers a comprehensive survey of the development, methodologies, and future directions in the field of single-image deraining. This research work systematically reviews various approaches for removing rain streaks and rain accumulation from degraded images, categorizing them mainly into model-based and data-driven methods.

Model-Based vs. Data-Driven Approaches

Initially, rainfall effects were managed using model-based methods. These methods relied on handcrafted priors and statistical modeling to separate rain effects from background scenes. Techniques such as image decomposition, sparse representation, and Gaussian mixture models were primarily employed. These methods proved to be useful in early stages, particularly when dealing with sparse and light rain streaks.

With advancements in deep learning, the focus gradually shifted to data-driven approaches. These methods harness Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and semi-supervised or unsupervised approaches to automatically learn feature representations of rain and backgrounds. The flexibility of deep learning models allows them to handle complex, heavy rain scenarios more effectively compared to traditional model-based techniques.

Performance Insights and Strong Numerical Results

The paper provides extensive performance evaluations of these methods using synthetic and real-world datasets. Deep learning-based methods like joint CNN architectures with attention mechanisms have demonstrated superior quantitative and qualitative results compared to their model-based counterparts. For instance, modern data-driven methods not only achieve high Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) but also perform better in retaining the details and enhancing the naturalness of the images as gauged by no-reference image quality metrics.

Critical Challenges and Limitations

A significant challenge identified is the gap between synthetic and real-world rain data. Most training datasets are synthesized based on certain assumptions and tend to lack the variability seen in actual rain conditions. This can lead to domain gaps where models trained on synthetic data may struggle with real images. Moreover, obtaining accurate ground truth for real-world rain scenes is practically infeasible, which complicates network training and evaluation.

Future Directions and Implications

The survey underscores several promising directions for future research:

1. Integration of Physics-Based Models: Enhancing models with physics-based understanding of rain formation can bridge gaps between synthetic and real rain scenes, thereby improving model reliability on real data.
2. Advanced Rain Synthesis Models: Developing more sophisticated and diverse rain models that capture a wide range of rain conditions could enhance model generalization.
3. Improved Evaluation Metrics: Current evaluation metrics do not fully align with human perception or high-level vision tasks impacted by rain. New metrics that better capture subjective quality and task performance are needed.
4. Real-Time and Composite Scenario Handling: Future deraining models should aim for efficient real-time processing to support practical applications such as surveillance. Furthermore, handling composite scenarios (e.g., night-time rains, mixed degradations) remains a critical area for exploration.

The paper serves as a foundational reference for researchers and practitioners in the computer vision community looking to develop advanced deraining techniques. By tracing the evolution from traditional to modern techniques and highlighting areas needing further research, it sets the stage for future advancements in this field.