SOTAVerified

Denoising

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 )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3476–3500 of 7282 papers

TitleStatusHype
Denoising Monte Carlo Renders with Diffusion ModelsCode0
Generative AI Models for Different Steps in Architectural Design: A Literature Review—0
NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and DenoisingCode0
QNCD: Quantization Noise Correction for Diffusion ModelsCode0
Debiasing Cardiac Imaging with Controlled Latent Diffusion ModelsCode0
DreamSalon: A Staged Diffusion Framework for Preserving Identity-Context in Editable Face Generation—0
Noise-Robust Keyword Spotting through Self-supervised PretrainingCode0
U-Sketch: An Efficient Approach for Sketch to Image Diffusion Models—0
FlexEdit: Flexible and Controllable Diffusion-based Object-centric Image Editing—0
LayoutFlow: Flow Matching for Layout Generation—0
ECNet: Effective Controllable Text-to-Image Diffusion Models—0
Image Deraining via Self-supervised Reinforcement Learning—0
DiffGaze: A Diffusion Model for Continuous Gaze Sequence Generation on 360° Images—0
Boosting Diffusion Models with Moving Average Sampling in Frequency Domain—0
Denoising Table-Text Retrieval for Open-Domain Question AnsweringCode0
Scalable Non-Cartesian Magnetic Resonance Imaging with R2D2—0
Global Point Cloud Registration Network for Large TransformationsCode0
GenesisTex: Adapting Image Denoising Diffusion to Texture Space—0
CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation—0
END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation—0
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational AutoencoderCode0
Paired Diffusion: Generation of related, synthetic PET-CT-Segmentation scans using Linked Denoising Diffusion Probabilistic Models—0
Annotated Biomedical Video Generation using Denoising Diffusion Probabilistic Models and Flow FieldsCode0
Multi-Scale Texture Loss for CT denoising with GANsCode0
Iso-Diffusion: Improving Diffusion Probabilistic Models Using the Isotropy of the Additive Gaussian Noise—0
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All datasetsDarmstadt Noise DatasetDIV2KDNDiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81—Unverified
2Pixel-shuffling DownsamplingPSNR38.4—Unverified
3TWSCPSNR37.93—Unverified
4CBDNet(Syn)PSNR37.57—Unverified
5MCWNNMPSNR37.38—Unverified
6Han et alPSNR35.95—Unverified
7FFDNetPSNR34.4—Unverified
8TNRDPSNR33.65—Unverified
9CDnCNN-BPSNR32.43—Unverified
10NLRNPSNR30.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61—Unverified