DRNet uses initialization-stage dynamic reparameterization, a guided DRMLP, and a wavelet encoder to deliver efficient all-in-one image restoration across multiple tasks.
Toward real-world single image deraining: A new benchmark and beyond
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
fields
cs.CV 5verdicts
UNVERDICTED 5roles
dataset 1polarities
use dataset 1representative citing papers
UENR-600K is a 600,000-frame synthetic dataset for nighttime video deraining that uses 3D rain particle simulation in Unreal Engine to enable better generalization to real scenes.
PVRF combines zero-shot VLM-based weather perception with perception-adaptive rectified flow refinement to achieve all-in-one adverse weather removal with improved fidelity and cross-dataset generalization.
RGSUD achieves SOTA unsupervised deraining by using IQA-based reward recycling and self-reinforcement to constrain optimization and improve pseudo-paired data quality.
RDBM reformulates generalized diffusion bridge SDEs to use distribution residuals for adaptive noise modulation, unifying prior bridge models as special cases and achieving SOTA on image restoration tasks.
citing papers explorer
-
DRNet: All-in-One Image Restoration via Prior-Guided Dynamic Reparameterization
DRNet uses initialization-stage dynamic reparameterization, a guided DRMLP, and a wavelet encoder to deliver efficient all-in-one image restoration across multiple tasks.
-
UENR-600K: A Large-Scale Physically Grounded Dataset for Nighttime Video Deraining
UENR-600K is a 600,000-frame synthetic dataset for nighttime video deraining that uses 3D rain particle simulation in Unreal Engine to enable better generalization to real scenes.
-
PVRF: All-in-one Adverse Weather Removal via Prior-modulated and Velocity-constrained Rectified Flow
PVRF combines zero-shot VLM-based weather perception with perception-adaptive rectified flow refinement to achieve all-in-one adverse weather removal with improved fidelity and cross-dataset generalization.
-
Unpaired Image Deraining Using Reward-Guided Self-Reinforcement Strategy
RGSUD achieves SOTA unsupervised deraining by using IQA-based reward recycling and self-reinforcement to constrain optimization and improve pseudo-paired data quality.
-
Residual Diffusion Bridge Model for Image Restoration
RDBM reformulates generalized diffusion bridge SDEs to use distribution residuals for adaptive noise modulation, unifying prior bridge models as special cases and achieving SOTA on image restoration tasks.