Rain Removal
125 papers with code • 1 benchmarks • 4 datasets
Libraries
Use these libraries to find Rain Removal models and implementations
Raindrop
Real Rain Dataset
RainDS
LasVR
Latest papers
InstructIR: High-Quality Image Restoration Following Human Instructions
All-In-One image restoration models can effectively restore images from various types and levels of degradation using degradation-specific information as prompts to guide the restoration model.
Improving Image Restoration through Removing Degradations in Textual Representations
To address the cross-modal assistance, we propose to map the degraded images into textual representations for removing the degradations, and then convert the restored textual representations into a guidance image for assisting image restoration.
ViStripformer: A Token-Efficient Transformer for Versatile Video Restoration
pp00704831/video-stripformer
•
•
Besides, ViStripformer is an effective and efficient transformer architecture with much lower memory usage than the vanilla transformer.
Exploring the potential of channel interactions for image restoration
c-yn/ChaIR
•
•
Knowledge-Based Systems 2023
Image restoration aims to reconstruct a clear image from a degraded observation.
Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge
Diffusion models possess powerful generative capabilities enabling the mapping of noise to data using reverse stochastic differential equations.
Textual Prompt Guided Image Restoration
motong-ai-studio/textpromptir
•
•
In this paper, an effective textual prompt guided image restoration model has been proposed.
Prompt-In-Prompt Learning for Universal Image Restoration
longzilicart/pip_universal
•
•
Second, we devise a novel prompt-to-prompt interaction module to fuse these two prompts into a universal restoration prompt.
Image Restoration via Frequency Selection
c-yn/FSNet
•
•
IEEE Transactions on Pattern Analysis and Machine Intelligence 2023
Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart.
TPSeNCE: Towards Artifact-Free Realistic Rain Generation for Deraining and Object Detection in Rain
We first introduce a Triangular Probability Similarity (TPS) constraint to guide the generated images toward clear and rainy images in the discriminator manifold, thereby minimizing artifacts and distortions during rain generation.
See SIFT in a Rain
jiuchu-buyan/See_SIFT_in_a_Rain
•
•
One is difference of Gaussian (DoG) pyramid recovery network (DPRNet) for SIFT detection, and the other gradients of Gaussian images recovery network (GGIRNet) for SIFT description.
