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 3851–3875 of 7282 papers

TitleStatusHype
Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition—0
CUR Transformer: A Convolutional Unbiased Regional Transformer for Image DenoisingCode0
Temporal Segment Transformer for Action Segmentation—0
Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties—0
Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI—0
Practical Knowledge Distillation: Using DNNs to Beat DNNs—0
DiffusioNeRF: Regularizing Neural Radiance Fields with Denoising Diffusion ModelsCode2
Controlled and Conditional Text to Image Generation with Diffusion Prior—0
PIFON-EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks—0
Entity-Level Text-Guided Image ManipulationCode1
Likelihood Annealing: Fast Calibrated Uncertainty for Regression—0
Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Photovoltaic Timeseries Data Imputation—0
LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and DeblurringCode1
Learning Gradually Non-convex Image Priors Using Score Matching—0
PC^2: Projection-Conditioned Point Cloud Diffusion for Single-Image 3D ReconstructionCode2
I2V: Towards Texture-Aware Self-Supervised Blind Denoising using Self-Residual Learning for Real-World Images—0
Restoration based Generative ModelsCode0
Unsupervised Out-of-Distribution Detection with Diffusion InpaintingCode1
Simulating analogue film damage to analyse and improve artefact restoration on high-resolution scansCode1
An Efficient and Robust Method for Chest X-Ray Rib Suppression that Improves Pulmonary Abnormality Diagnosis—0
One-Pot Multi-Frame Denoising—0
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE—0
When Visible-to-Thermal Facial GAN Beats Conditional Diffusion—0
Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be ConsistentCode1
AliasNet: Alias Artefact Suppression Network for Accelerated Phase-Encode MRI—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