Rain Removal
122 papers with code • 1 benchmarks • 4 datasets
Libraries
Use these libraries to find Rain Removal models and implementationsMost implemented papers
SDNet: mutil-branch for single image deraining using swin
The former implements the basic rain pattern feature extraction, while the latter fuses different features to further extract and process the image features.
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.
Deep Joint Rain Detection and Removal from a Single Image
Based on the first model, we develop a multi-task deep learning architecture that learns the binary rain streak map, the appearance of rain streaks, and the clean background, which is our ultimate output.
Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset
Second, to better cover the stochastic distribution of real rain streaks, we propose a novel SPatial Attentive Network (SPANet) to remove rain streaks in a local-to-global manner.
Is it Raining Outside? Detection of Rainfall using General-Purpose Surveillance Cameras
In this paper, we design a system for the detection of rainfall by the use of surveillance cameras.
Detail-recovery Image Deraining via Context Aggregation Networks
This paper looks at this intriguing question: are single images with their details lost during deraining, reversible to their artifact-free status?
EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image Deraining
To fill this gap, in this paper, we regard the single-image deraining as a general image-enhancing problem and originally propose a model-free deraining method, i. e., EfficientDeRain, which is able to process a rainy image within 10~ms (i. e., around 6~ms on average), over 80 times faster than the state-of-the-art method (i. e., RCDNet), while achieving similar de-rain effects.
HINet: Half Instance Normalization Network for Image Restoration
Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to boost the performance of image restoration networks.
Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration
Our approach, named Model Contrastive Learning for Image Restoration (MCLIR), rejuvenates latency models as negative models, making it compatible with diverse image restoration tasks.
Semi-supervised Transfer Learning for Image Rain Removal
However, previous deep learning methods need to pre-collect a large set of image pairs with/without synthesized rain for training, which tends to make the neural network be biased toward learning the specific patterns of the synthesized rain, while be less able to generalize to real test samples whose rain types differ from those in the training data.