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 1976–2000 of 7282 papers

TitleStatusHype
A Self-Supervised Method for Attenuating Seismic Random and Tracewise Coherent Noise under the Non-Pixelwise Independence AssumptionCode0
Burger: Robust Graph Denoising-augmentation Fusion and Multi-semantic Modeling in Social Recommendation—0
ProFashion: Prototype-guided Fashion Video Generation with Multiple Reference Images—0
Towards order of magnitude X-ray dose reduction in breast cancer imaging using phase contrast and deep denoisingCode0
Auto Tensor Singular Value Thresholding: A Non-Iterative and Rank-Free Framework for Tensor Denoising—0
Automated Learning of Semantic Embedding Representations for Diffusion Models—0
Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions—0
Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review—0
Denoising Diffusion Probabilistic Models for Coastal Inundation Forecasting—0
MDAA-Diff: CT-Guided Multi-Dose Adaptive Attention Diffusion Model for PET Denoising—0
Overcoming Dimensional Factorization Limits in Discrete Diffusion Models through Quantum Joint Distribution LearningCode0
Graffe: Graph Representation Learning via Diffusion Probabilistic Models—0
EDmamba: A Simple yet Effective Event Denoising Method with State Space Model—0
Inter-Diffusion Generation Model of Speakers and Listeners for Effective Communication—0
Score-based Self-supervised MRI Denoising—0
DiffusionSfM: Predicting Structure and Motion via Ray Origin and Endpoint Diffusion—0
Riemannian Denoising Diffusion Probabilistic Models—0
CountDiffusion: Text-to-Image Synthesis with Training-Free Counting-Guidance Diffusion—0
Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI ReconstructionCode0
FEMSN: Frequency-Enhanced Multiscale Network for fault diagnosis of rotating machinery under strong noise environments—0
Spectral and Temporal Denoising for Differentially Private Optimization—0
Knowledge Distillation for Speech Denoising by Latent Representation Alignment with Cosine Distance—0
MRI motion correction via efficient residual-guided denoising diffusion probabilistic models—0
Enhancing Glass Defect Detection with Diffusion Models: Addressing Imbalanced Datasets in Manufacturing Quality Control—0
FlexiAct: Towards Flexible Action Control in Heterogeneous Scenarios—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