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 4801–4825 of 7282 papers

TitleStatusHype
ReFrame: Layer Caching for Accelerated Inference in Real-Time Rendering—0
A comparison study of CNN denoisers on PRNU extraction—0
Regional Priority Based Anomaly Detection using Autoencoders—0
Unsupervised Abnormality Detection through Mixed Structure Regularization (MSR) in Deep Sparse Autoencoders—0
Unsupervised Accelerated MRI Reconstruction via Ground-Truth-Free Flow Matching—0
Regularization by denoising: Bayesian model and Langevin-within-split Gibbs sampling—0
Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition—0
Regularization by Denoising via Fixed-Point Projection (RED-PRO)—0
Regularized estimation of image statistics by Score Matching—0
Regularizing linear inverse problems with convolutional neural networks—0
Zero-Shot Mono-to-Binaural Speech Synthesis—0
Regularizing Trajectory Optimization with Denoising Autoencoders—0
Regular Time-series Generation using SGM—0
ReinFlow: Fine-tuning Flow Matching Policy with Online Reinforcement Learning—0
Reinforced Label Denoising for Weakly-Supervised Audio-Visual Video Parsing—0
Reinforcement-based denoising of distantly supervised NER with partial annotation—0
RPT: Relational Pre-trained Transformer Is Almost All You Need towards Democratizing Data Preparation—0
Relation Mention Extraction from Noisy Data with Hierarchical Reinforcement Learning—0
Relationship Quantification of Image Degradations—0
RELD: Regularization by Latent Diffusion Models for Image Restoration—0
Reliability-based Mesh-to-Grid Image Reconstruction—0
Reliable Deep Diffusion Tensor Estimation: Rethinking the Power of Data-Driven Optimization Routine—0
Relightable 3D Head Portraits from a Smartphone Video—0
Relightify: Relightable 3D Faces from a Single Image via Diffusion Models—0
Remaining useful life prediction of rolling bearings based on refined composite multi-scale attention entropy and dispersion entropy—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