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 1226–1250 of 7282 papers

TitleStatusHype
MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality SequencesCode1
Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological ReportCode1
Representation Learning with Diffusion ModelsCode1
BirdSoundsDenoising: Deep Visual Audio Denoising for Bird SoundsCode1
TransFusion: Transcribing Speech with Multinomial DiffusionCode1
3D Brain and Heart Volume Generative Models: A SurveyCode1
GENIE: Higher-Order Denoising Diffusion SolversCode1
Markup-to-Image Diffusion Models with Scheduled SamplingCode1
A generic diffusion-based approach for 3D human pose prediction in the wildCode1
Denoising Masked AutoEncoders Help Robust ClassificationCode1
CLIP-Diffusion-LM: Apply Diffusion Model on Image CaptioningCode1
FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck EquationCode1
Label-Driven Denoising Framework for Multi-Label Few-Shot Aspect Category DetectionCode1
STaSy: Score-based Tabular data SynthesisCode1
Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning RulesCode1
clip2latent: Text driven sampling of a pre-trained StyleGAN using denoising diffusion and CLIPCode1
Diffusion Models for Graphs Benefit From Discrete State SpacesCode1
Accurate Image Restoration with Attention Retractable TransformerCode1
OCD: Learning to Overfit with Conditional Diffusion ModelsCode1
TT-NF: Tensor Train Neural FieldsCode1
Denoising MCMC for Accelerating Diffusion-Based Generative ModelsCode1
Denoising of 3D MR images using a voxel-wise hybrid residual MLP-CNN model to improve small lesion diagnostic confidenceCode1
Multi-stage image denoising with the wavelet transformCode1
JPEG Artifact Correction using Denoising Diffusion Restoration ModelsCode1
MIDMs: Matching Interleaved Diffusion Models for Exemplar-based Image TranslationCode1
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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