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

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Showing 701–725 of 7282 papers

TitleStatusHype
Diff-UNet: A Diffusion Embedded Network for Volumetric SegmentationCode1
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstructionCode1
Action-Conditioned 3D Human Motion Synthesis with Transformer VAECode1
DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic ModelsCode1
DialogLM: Pre-trained Model for Long Dialogue Understanding and SummarizationCode1
RDEIC: Accelerating Diffusion-Based Extreme Image Compression with Relay Residual DiffusionCode1
DiAMoNDBack: Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein TracesCode1
Diffusion-based Pose Refinement and Muti-hypothesis Generation for 3D Human Pose EstimaitonCode1
DFormer: Diffusion-guided Transformer for Universal Image SegmentationCode1
Devil is in the Uniformity: Exploring Diverse Learners within Transformer for Image RestorationCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly DetectionCode1
Designing and Training of A Dual CNN for Image DenoisingCode1
DETA: Denoised Task Adaptation for Few-Shot LearningCode1
DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion DelineationCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse ProblemsCode1
Diffusion Model Based Visual Compensation Guidance and Visual Difference Analysis for No-Reference Image Quality AssessmentCode1
Diffusion Models as Network Optimizers: Explorations and AnalysisCode1
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual ApproximatorsCode1
DiffAR: Denoising Diffusion Autoregressive Model for Raw Speech Waveform GenerationCode1
Asymmetric Mask Scheme for Self-Supervised Real Image DenoisingCode1
Diffusion Action SegmentationCode1
Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain ImagesCode1
Denoising Relation Extraction from Document-level Distant SupervisionCode1
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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