Multi-Scale Progressive Fusion Network for Single Image Deraining

@article{Jiang2020MultiScalePF,
  title={Multi-Scale Progressive Fusion Network for Single Image Deraining},
  author={Kui Jiang and Zhongyuan Wang and Peng Yi and Chen Chen and Baojin Huang and Yimin Luo and Jiayi Ma and Junjun Jiang},
  journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020},
  pages={8343-8352}
}
Rain streaks in the air appear in various blurring degrees and resolutions due to different distances from their positions to the camera. Similar rain patterns are visible in a rain image as well as its multi-scale (or multi-resolution) versions, which makes it possible to exploit such complementary information for rain streak representation. In this work, we explore the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep… 
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204 Citations
Highly Influential Citations
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Background Citations
109
Methods Citations
82
Results Citations
3

204 Citations

Multi-Scale Hourglass Hierarchical Fusion Network for Single Image Deraining

Single image deraining has become a vital preprocessing step in the practical applications for object detection, recognition, and tracking tasks under rainy situations.

Single image deraining using multi‐stage and multi‐scale joint channel coordinate attention fusion network

This work proposes an effective algorithm, called multi‐stage and multi‐scale joint channel coordinate attention fusion network (MMAFN), both of which use an encoder‐decoder network to extract features and integrates the features of the former to further refine features.

Multi-Scale Channel Transformer Network for Single Image Deraining

This study proposes a novel Channel Transformer, which performs self-attention in the channel direction instead of the spatial direction, and first incorporates multiple channel transformer blocks into a multi- scale architecture to extract multi-scale contexts and exploit channel long-dependence.

Multi-scale Residual Aggregation Deraining Network with Spatial Context-aware Pooling and Activation

This study proposes a novel multi-scale residual aggregation network, to effectively solve the single image deraining problem and proposes the spatial context-aware pooling and activation method (SCAP and activation) for incorporating with the authors' deraining network to boost performance.

Contrastive learning-based generative network for single image deraining

An innovative contrastive learning strategy is proposed and applied to each stage of the proposed CLGNet to enhance the decoupling ability of the encoder and help the model recognize complex rain patterns.

Multifocal Attention-Based Cross-Scale Network for Image De-raining

An effective multifocal attention-based cross-scale network is constructed, which exhaustively utilizes the cross- scale correlations of both rain streaks and background, to achieve image de-raining.

Multistage Feature Complimentary Network for Single-Image Deraining

A multistage framework based on progressive restoration combined with recurrent neural network and feature complementarity technology to remove rain streak from single images can remove rain more completely, preserve more background details, and achieve better visual effects compared with some popular single-image deraining methods.

Cascaded Attention Guidance Network for Single Rainy Image Restoration

An advanced cascaded attention guidance network, dubbed as CAG-Net, is formulated and designed as a three-stage model, and is demonstrated to produce significantly better results than state-of-the-art models.

MARA-Net: Single Image Deraining Network with Multi-level connection and Adaptive Regional Attention

A multi-level connection and adaptive regional attention network (MARA-Net) to properly restore the original background textures in rainy images and demonstrates that the proposed model significantly outperforms existing state-of-the-art models.

Decomposition Makes Better Rain Removal: An Improved Attention-Guided Deraining Network

An improved non-local block is designed to exploit the self-similarity of rain information by learning the holistic spatial feature correlations while reducing the calculation complexity.
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References

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