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 926–950 of 7282 papers

TitleStatusHype
Far3D: Expanding the Horizon for Surround-view 3D Object DetectionCode1
Learning A Coarse-to-Fine Diffusion Transformer for Image RestorationCode1
Self-Reference Deep Adaptive Curve Estimation for Low-Light Image EnhancementCode1
Denoising Diffusion Probabilistic Model for Retinal Image Generation and SegmentationCode1
Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image SegmentationCode1
Precipitation nowcasting with generative diffusion modelsCode1
Unsupervised Image Denoising in Real-World Scenarios via Self-Collaboration Parallel Generative Adversarial BranchesCode1
Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh ReconstructionCode1
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE SolversCode1
Masked Diffusion as Self-supervised Representation LearnerCode1
Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image DenoisingCode1
NNVISR: Bring Neural Network Video Interpolation and Super Resolution into Video Processing FrameworkCode1
Generative Approach for Probabilistic Human Mesh Recovery using Diffusion ModelsCode1
DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion DelineationCode1
Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain ImagesCode1
Patched Denoising Diffusion Models For High-Resolution Image SynthesisCode1
EC-Conf: An Ultra-fast Diffusion Model for Molecular Conformation Generation with Equivariant ConsistencyCode1
Random Sub-Samples Generation for Self-Supervised Real Image DenoisingCode1
Towards General Low-Light Raw Noise Synthesis and ModelingCode1
DiffProsody: Diffusion-based Latent Prosody Generation for Expressive Speech Synthesis with Prosody Conditional Adversarial TrainingCode1
Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion ModelsCode1
Crystal Structure Prediction by Joint Equivariant DiffusionCode1
Ultrasound Image Reconstruction with Denoising Diffusion Restoration ModelsCode1
Artifact Restoration in Histology Images with Diffusion Probabilistic ModelsCode1
Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEGCode1
Show:102550
← PrevPage 38 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