Denoising
1901 papers with code • 5 benchmarks • 20 datasets
Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.
( Image credit: Beyond a Gaussian Denoiser )
Benchmarks
These leaderboards are used to track progress in Denoising
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Libraries
Use these libraries to find Denoising models and implementations
SIDD
SID
CBSD68
PolyU
DND
CRVD
iris
FMD
Subtasks
Latest papers with no code
Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models
In this work, we propose to detect OOD molecules by adopting an auxiliary diffusion model-based framework, which compares similarities between input molecules and reconstructed graphs.
OccGen: Generative Multi-modal 3D Occupancy Prediction for Autonomous Driving
Existing solutions for 3D semantic occupancy prediction typically treat the task as a one-shot 3D voxel-wise segmentation perception problem.
DENOISER: Rethinking the Robustness for Open-Vocabulary Action Recognition
The denoised text classes help OVAR models classify visual samples more accurately; in return, classified visual samples help better denoising.
A sensitivity analysis to quantify the impact of neuroimaging preprocessing strategies on subsequent statistical analyses
Even though novel imaging techniques have been successful in studying brain structure and function, the measured biological signals are often contaminated by multiple sources of noise, arising due to e. g. head movements of the individual being scanned, limited spatial/temporal resolution, or other issues specific to each imaging technology.
Score matching for sub-Riemannian bridge sampling
Simulation of conditioned diffusion processes is an essential tool in inference for stochastic processes, data imputation, generative modelling, and geometric statistics.
GLoD: Composing Global Contexts and Local Details in Image Generation
However, simultaneous control over both global contexts (e. g., object layouts and interactions) and local details (e. g., colors and emotions) still remains a significant challenge.
ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model
Generating trajectory data is among promising solutions to addressing privacy concerns, collection costs, and proprietary restrictions usually associated with human mobility analyses.
Towards Better Adversarial Purification via Adversarial Denoising Diffusion Training
Empirical results show that ADDT improves the robustness of DBP models.
Non-Uniform Exposure Imaging via Neuromorphic Shutter Control
To address this challenge, we propose a novel Neuromorphic Shutter Control (NSC) system to avoid motion blurs and alleviate instant noises, where the extremely low latency of events is leveraged to monitor the real-time motion and facilitate the scene-adaptive exposure.
Accelerating Image Generation with Sub-path Linear Approximation Model
Diffusion models have significantly advanced the state of the art in image, audio, and video generation tasks.
