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 3651–3675 of 7282 papers

TitleStatusHype
Two-stage Denoising Diffusion Model for Source Localization in Graph Inverse Problems—0
HandCT: hands-on computational dataset for X-Ray Computed Tomography and Machine-Learning—0
Unsupervised Image Denoising with Score Function—0
Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion ModelsCode2
DAS-N2N: Machine learning Distributed Acoustic Sensing (DAS) signal denoising without clean dataCode1
OVTrack: Open-Vocabulary Multiple Object TrackingCode1
Fault Tolerant FPGA Implementation on Redundancy Techniques and ECG Denoising—0
ASIC Implementation of Denoising Filters for Pacemakers—0
Within-Camera Multilayer Perceptron DVS DenoisingCode1
Delta Denoising Score—0
Towards Controllable Diffusion Models via Reward-Guided Exploration—0
Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks with Soft-Thresholding—0
Survey on LiDAR Perception in Adverse Weather Conditions—0
DDT: Dual-branch Deformable Transformer for Image DenoisingCode1
EWT: Efficient Wavelet-Transformer for Single Image Denoising—0
Gated Multi-Resolution Transfer Network for Burst Restoration and EnhancementCode1
Soundini: Sound-Guided Diffusion for Natural Video EditingCode1
An Edit Friendly DDPM Noise Space: Inversion and ManipulationsCode2
Discovering Structure From Corruption for Unsupervised Image Reconstruction—0
InterGen: Diffusion-based Multi-human Motion Generation under Complex InteractionsCode2
SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion ModelsCode1
Inhomogeneous graph trend filtering via a l2,0 cardinality penalty—0
A comparative study between paired and unpaired Image Quality Assessment in Low-Dose CT DenoisingCode0
Diffusion Models for Constrained DomainsCode1
Diffusion Recommender ModelCode2
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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