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 1376–1400 of 7282 papers

TitleStatusHype
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic ModelsCode1
ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NERCode1
DiffDA: a Diffusion Model for Weather-scale Data AssimilationCode1
Inference-Time Text-to-Video Alignment with Diffusion Latent Beam SearchCode1
Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic ModelingCode1
-Diff: Infinite Resolution Diffusion with Subsampled Mollified StatesCode1
Input Perturbation Reduces Exposure Bias in Diffusion ModelsCode1
DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic ModelsCode1
COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video EditingCode1
DialogLM: Pre-trained Model for Long Dialogue Understanding and SummarizationCode1
CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack SegmentationCode1
Interpolating between Images with Diffusion ModelsCode1
Interpretable Unsupervised Joint Denoising and Enhancement for Real-World low-light ScenariosCode1
DiAMoNDBack: Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein TracesCode1
Invariance Matters: Empowering Social Recommendation via Graph Invariant LearningCode1
DFormer: Diffusion-guided Transformer for Universal Image SegmentationCode1
Characteristic Guidance: Non-linear Correction for Diffusion Model at Large Guidance ScaleCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
Invertible Denoising Network: A Light Solution for Real Noise RemovalCode1
DETA: Denoised Task Adaptation for Few-Shot LearningCode1
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly DetectionCode1
Iterative Gaussianization: from ICA to Random RotationsCode1
IterativePFN: True Iterative Point Cloud FilteringCode1
Joint Frequency and Image Space Learning for MRI Reconstruction and AnalysisCode1
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual ApproximatorsCode1
Show:102550
← PrevPage 56 of 292Next →
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