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Found 6 papers, 4 papers with code
Detail-recovery Image Deraining via Dual Sample-augmented Contrastive Learning
To bridge the two domain gaps, we propose a semi-supervised detail-recovery image deraining network (Semi-DRDNet) with dual sample-augmented contrastive learning.
Direction-aware Feature-level Frequency Decomposition for Single Image Deraining
Second, we further establish communication channels between low-frequency maps and high-frequency maps to interactively capture structures from high-frequency maps and add them back to low-frequency maps and, simultaneously, extract details from low-frequency maps and send them back to high-frequency maps, thereby removing rain streaks while preserving more delicate features in the input image.
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?
MBA-RainGAN: Multi-branch Attention Generative Adversarial Network for Mixture of Rain Removal from Single Images
We observe three intriguing phenomenons that, 1) rain is a mixture of raindrops, rain streaks and rainy haze; 2) the depth from the camera determines the degrees of object visibility, where objects nearby and faraway are visually blocked by rain streaks and rainy haze, respectively; and 3) raindrops on the glass randomly affect the object visibility of the whole image space.
DRD-Net: Detail-recovery Image Deraining via Context Aggregation Networks
We have validated our approach on four recognized datasets (three synthetic and one real-world).
Convolutional Neural Network with Median Layers for Denoising Salt-and-Pepper Contaminations
We propose a deep fully convolutional neural network with a new type of layer, named median layer, to restore images contaminated by the salt-and-pepper (s&p) noise.
