• Corpus ID: 237260106

Structure-Preserving Deraining with Residue Channel Prior Guidance

  title={Structure-Preserving Deraining with Residue Channel Prior Guidance},
  author={Qiaosi Yi and Juncheng Li and Qi Dai and Faming Fang and Guixu Zhang and Tieyong Zeng},
Single image deraining is important for many high-level computer vision tasks since the rain streaks can severely degrade the visibility of images, thereby affecting the recognition and analysis of the image. Recently, many CNN-based methods have been proposed for rain removal. Although these methods can remove part of the rain streaks, it is difficult for them to adapt to real-world scenarios and restore high-quality rain-free images with clear and accurate structures. To solve this problem… 
Uncertainty-Aware Cascaded Dilation Filtering for High-Efficiency Deraining
  • Qing Guo, Jingyang Sun, +4 authors Song Wang
  • Computer Science, Engineering
  • 2022
A simple yet efficient deraining method is proposed by formulating deraining as a predictive filtering problem without complex rain model assumptions that outperforms baseline methods on four single-image deraining datasets and one video deraining dataset in terms of both recovery quality and speed.
Revisiting Global Statistics Aggregation for Improving Image Restoration
This paper shows that statistics aggregated on the patches-based/entire-image-based feature in the training/testing phase respectively may distribute very differently and lead to performance degradation in image restorers, and proposes a simple approach, Test-time Local Statistics Converter (TLSC), that replaces the region of statistics aggregation operation from global to local only in the test time.


Single Image Deraining via Recurrent Hierarchy Enhancement Network
A novel network named Recurrent Hierarchy Enhancement Network (ReHEN) to remove rain streaks from rainy images stage by stage is proposed, which outperforms the state-of-the-art methods considerably.
Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining
A novel deep network architecture based on deep convolutional and recurrent neural networks for single image deraining based on contextual information is proposed and outperforms the state-of-the-art approaches under all evaluation metrics.
Spatial Attentive Single-Image Deraining With a High Quality Real Rain Dataset
A semi-automatic method that incorporates temporal priors and human supervision to generate a high-quality clean image from each input sequence of real rain images is proposed, and a novel SPatial Attentive Network (SPANet) is proposed to remove rain streaks in a local-to-global manner.
Single image rain streaks removal: a review and an exploration
A specific coarse-to-fine deraining network architecture is built, which can finely deliver the rain structures and progressively removes rain streaks from the input image, accordingly, and possesses better generalization capability on real rainy images, implying its potential usefulness for this task.
A Comprehensive Benchmark Analysis of Single Image Deraining: Current Challenges and Future Perspectives
This paper presents a comprehensive study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark consisting of both synthetic and real-world rainy images of various rain types, and indicates the gap between the achievable performance on synthetic rainy images and the practical demand on real- world images.
Scale-Free Single Image Deraining Via Visibility-Enhanced Recurrent Wavelet Learning
To handle heavy rain cases where rain streak accumulation is presented, a detail appearing rain accumulation removal is constructed to not only improve the visibility but also enhance the details in dark regions.
Single Image Deraining: From Model-Based to Data-Driven and Beyond
This paper presents milestones of single-image deraining methods, reviews a broad selection of previous works in different categories, and provides insights on the historical development route from the model-based to data-driven methods.
Removing Rain from Single Images via a Deep Detail Network
A deep detail network is proposed to directly reduce the mapping range from input to output, which makes the learning process easier and significantly outperforms state-of-the-art methods on both synthetic and real-world images in terms of both qualitative and quantitative measures.
Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal
We introduce a deep network architecture called DerainNet for removing rain streaks from an image. Based on the deep convolutional neural network (CNN), we directly learn the mapping relationship
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
This work proposes a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections, and proposes a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels.