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 876–900 of 7282 papers

TitleStatusHype
DiffuseReg: Denoising Diffusion Model for Obtaining Deformation Fields in Unsupervised Deformable Image RegistrationCode1
FD-SOS: Vision-Language Open-Set Detectors for Bone Fenestration and Dehiscence Detection from Intraoral ImagesCode1
Fast mesh denoising with data driven normal filtering using deep variational autoencodersCode1
Fast Inference in Denoising Diffusion Models via MMD FinetuningCode1
Fast Monte Carlo Rendering via Multi-Resolution SamplingCode1
An Analysis and Mitigation of the Reversal CurseCode1
Beyond Image Prior: Embedding Noise Prior into Conditional Denoising TransformerCode1
Adversarial score matching and improved sampling for image generationCode1
Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse RepresentationsCode1
FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video GenerationCode1
Diffusion-based Pose Refinement and Muti-hypothesis Generation for 3D Human Pose EstimaitonCode1
Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based PenaltiesCode1
Adversarial Schrödinger Bridge MatchingCode1
Beyond Surface Statistics: Scene Representations in a Latent Diffusion ModelCode1
3D Vessel Graph Generation Using Denoising DiffusionCode1
Beyond the Spectrum: Detecting Deepfakes via Re-SynthesisCode1
Customized Generation Reimagined: Fidelity and Editability HarmonizedCode1
Input Perturbation Reduces Exposure Bias in Diffusion ModelsCode1
Adversarial purification with Score-based generative modelsCode1
DDT: Dual-branch Deformable Transformer for Image DenoisingCode1
A Continuous Time Framework for Discrete Denoising ModelsCode1
Controlling Latent Diffusion Using Latent CLIPCode1
DenoiseRep: Denoising Model for Representation LearningCode1
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse ProblemsCode1
FastHyMix: Fast and Parameter-free Hyperspectral Image Mixed Noise RemovalCode1
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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