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Related Experiment Video

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Scale-Free Single Image Deraining Via Visibility-Enhanced Recurrent Wavelet Learning.

Wenhan Yang, Jiaying Liu, Shuai Yanga

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 15, 2019

Summary

This study introduces a novel recurrent neural network for effective single-image rain removal, even with large streaks and heavy accumulation. The method adapts to various streak sizes, significantly improving performance on real-world rainy images.

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Area of Science:

Computer Vision
Image Processing

Background:

Single image rain removal is challenging due to varying streak sizes and accumulation.
Existing methods struggle with large rain streaks and heavy rain, leading to poor performance.

Purpose of the Study:

To develop an effective single-image rain removal method for large rain streaks and accumulation.
To improve the adaptability of rain removal networks to different streak sizes.

Main Methods:

A hierarchical wavelet transform representation is embedded into a recurrent rain removal process.
A dilated residual dense network is utilized for recurrent detail recovery.
A detail-appearing rain accumulation removal module is introduced for heavy rain scenarios.

Main Results:

The proposed network demonstrates adaptability to larger rain streaks than those seen during training.
Significant performance improvements are observed on synthetic and real images with large streaks and heavy accumulation.
The method outperforms state-of-the-art techniques in challenging rain removal scenarios.

Conclusions:

The novel recurrent approach effectively handles large rain streaks and accumulation.
The hierarchical wavelet representation and dilated residual dense network contribute to superior rain removal.
This work advances single-image deraining, particularly for adverse weather conditions.