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 2026–2050 of 7282 papers

TitleStatusHype
PatchScaler: An Efficient Patch-Independent Diffusion Model for Image Super-ResolutionCode1
Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference—0
Uncovering cognitive taskonomy through transfer learning in masked autoencoder-based fMRI reconstruction—0
Diffusion Bridge Implicit ModelsCode2
ExactDreamer: High-Fidelity Text-to-3D Content Creation via Exact Score MatchingCode0
Score Distillation via Reparametrized DDIM—0
Towards Understanding the Working Mechanism of Text-to-Image Diffusion Model—0
Denoising LM: Pushing the Limits of Error Correction Models for Speech Recognition—0
NIVeL: Neural Implicit Vector Layers for Text-to-Vector Generation—0
ArtWeaver: Advanced Dynamic Style Integration via Diffusion Model—0
Nonlinear denoising score matching for enhanced learning of structured distributions—0
Learning to Discretize Denoising Diffusion ODEsCode1
Taming Score-Based Diffusion Priors for Infinite-Dimensional Nonlinear Inverse Problems—0
I^2VC: A Unified Framework for Intra- & Inter-frame Video CompressionCode1
Combining Denoising Autoencoders with Contrastive Learning to fine-tune Transformer ModelsCode0
Visual Echoes: A Simple Unified Transformer for Audio-Visual Generation—0
Nuclear Norm Regularization for Deep Learning—0
Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image GenerationCode2
Adapting to Unknown Low-Dimensional Structures in Score-Based Diffusion Models—0
Adversarial Schrödinger Bridge MatchingCode1
Bracket Diffusion: HDR Image Generation by Consistent LDR Denoising—0
Multistable Shape from Shading Emerges from Patch Diffusion—0
Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic ModelsCode0
Score-based Generative Models with Adaptive Momentum—0
Design Editing for Offline Model-based Optimization—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