Deep Leaning Based Frequency-Aware Single Image Deraining by Extracting Knowledge from Rain and Background

He Yuhong; Zeng Tao; Xiong YeLi JialuWei Haoran

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Deep Leaning Based Frequency-Aware Single Image Deraining by Extracting Knowledge from Rain and Background

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摘要

Due to the requirement of video surveillance, machine learning-based single image deraining has become a research hotspot in recent years. In order to efficiently obtain rain removal images that contain more detailed information, this paper proposed a novel frequency-aware single image deraining network via the separation of rain and background. For the rainy images, most of the background key information belongs to the low-frequency components, while the high-frequency components are mixed by background image details and rain streaks. This paper attempted to decouple background image details from high frequency components under the guidance of the restored low frequency components. Compared with existing approaches, the proposed network has three major contributions. (1) A residual dense network based on Discrete Wavelet Transform (DWT) was proposed to study the rainy image background information. (2) The frequency channel attention module was introduced into the adaptive decoupling of high-frequency image detail signals. (3) A fusion module was introduced that contains the attention mechanism to make full use of the multi receptive fields information using a two-branch structure, using the context information in a large area. The proposed approach was evaluated using several representative datasets. Experimental results shows this proposed approach outperforms other state-of-the-art deraining algorithms.

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《Machine Learning and Knowledge Extraction》 |2022年第3期|738-752|共15页
作者
He Yuhong; Zeng Tao; Xiong YeLi JialuWei Haoran;
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作者单位

Univ Texas Dallas;

East China Jiaotong Univ;

Univ Noth Texas;

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原文格式 PDF
正文语种英语
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deep learning; machine learning; knowledge extraction; single image deraining; frequency aware; discrete wavelet transform; attention mechanism; STREAKS REMOVAL;

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机译：New Findings Reported from University of Electronic Science and Technology of China Describe Advances in Knowledge Engineering (A Priori-guided Multi-layer Rain-aware Network for Single Image Deraining)
2. Findings on Networks Reported by Investigators at Shenzhen University (Adaptive Weighted Rain Streaks Model-driven Deep Network for Single Image Deraining) [J] . Network Daily News . 2023,第14期

机译：Findings on Networks Reported by Investigators at Shenzhen University (Adaptive Weighted Rain Streaks Model-driven Deep Network for Single Image Deraining)
3. Adaptive weighted rain streaks model-driven deep network for single image deraining [J] . Ya-Nan Zhang, Linlin Shen, Zhihui Lai Expert Systems with Application . 2023,第7期

机译：Adaptive weighted rain streaks model-driven deep network for single image deraining

Deep Leaning Based Frequency-Aware Single Image Deraining by Extracting Knowledge from Rain and Background

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