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 2701–2725 of 7282 papers

TitleStatusHype
Robust multi-coil MRI reconstruction via self-supervised denoisingCode0
Posterior Sampling for Random Noise Attenuation via Score-based Generative ModelsCode0
MSSIDD: A Benchmark for Multi-Sensor DenoisingCode0
KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems—0
Dataset Distillers Are Good Label Denoisers In the WildCode0
Controlling Diversity at Inference: Guiding Diffusion Recommender Models with Targeted Category PreferencesCode0
Time Step Generating: A Universal Synthesized Deepfake Image DetectorCode0
GeomCLIP: Contrastive Geometry-Text Pre-training for MoleculesCode0
MaskMedPaint: Masked Medical Image Inpainting with Diffusion Models for Mitigation of Spurious CorrelationsCode0
Test-time Conditional Text-to-Image Synthesis Using Diffusion Models—0
Neighboring Slice Noise2Noise: Self-Supervised Medical Image Denoising from Single Noisy Image Volume—0
Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolution—0
Probabilistic Prior Driven Attention Mechanism Based on Diffusion Model for Imaging Through Atmospheric Turbulence—0
Learning Generalizable 3D Manipulation With 10 DemonstrationsCode0
Enhancing Diffusion Posterior Sampling for Inverse Problems by Integrating Crafted Measurements—0
DR-BFR: Degradation Representation with Diffusion Models for Blind Face Restoration—0
Imagine-2-Drive: Leveraging High-Fidelity World Models via Multi-Modal Diffusion Policies—0
ColorEdit: Training-free Image-Guided Color editing with diffusion model—0
VidMan: Exploiting Implicit Dynamics from Video Diffusion Model for Effective Robot Manipulation—0
Iterative tomographic reconstruction with TV prior for low-dose CBCT dental imaging—0
Video Denoising in Fluorescence Guided Surgery—0
GAN-Based Architecture for Low-dose Computed Tomography Imaging Denoising—0
DeBaTeR: Denoising Bipartite Temporal Graph for Recommendation—0
A survey of probabilistic generative frameworks for molecular simulationsCode0
EEG-Based Speech Decoding: A Novel Approach Using Multi-Kernel Ensemble Diffusion Models—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