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Single Image Deraining: From Model-Based to Data-Driven and Beyond.

Wenhan Yang, Robby T Tan, Shiqi Wang

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 6, 2020

Summary

This summary is machine-generated.

This survey reviews single-image deraining methods over the last decade, covering both model-based and deep learning approaches. It highlights the evolution from traditional techniques to advanced neural networks for rain removal.

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Single-image deraining aims to remove rain effects from images.
Early methods relied on cost functions and priors.
Deep learning has significantly advanced deraining since 2017.

Purpose of the Study:

To provide a comprehensive survey of single-image deraining methods.
To analyze the historical development from model-based to data-driven approaches.
To summarize performance comparisons and discuss future directions.

Main Methods:

Categorization into model-based and data-driven approaches.
Review of rain appearance models, network architectures, constraints, and loss functions.
Analysis of training datasets and performance evaluation metrics.

Main Results:

Detailed overview of deraining techniques over the past decade.
Identification of key milestones and advancements in the field.
Quantitative and qualitative performance comparisons of various methods.

Conclusions:

Deep learning methods have shown impressive performance in single-image deraining.
Understanding the evolution from model-based to data-driven techniques is crucial.
Future research directions in deraining are identified.