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 1051–1075 of 7282 papers

TitleStatusHype
Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentationCode1
DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic ModelsCode1
3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data InversionCode1
Style-A-Video: Agile Diffusion for Arbitrary Text-based Video Style TransferCode1
Dual Residual Attention Network for Image DenoisingCode1
A Variational Perspective on Solving Inverse Problems with Diffusion ModelsCode1
SST-ReversibleNet: Reversible-prior-based Spectral-Spatial Transformer for Efficient Hyperspectral Image ReconstructionCode1
Efficient and Degree-Guided Graph Generation via Discrete Diffusion ModelingCode1
2D medical image synthesis using transformer-based denoising diffusion probabilistic modelCode1
DisenBooth: Identity-Preserving Disentangled Tuning for Subject-Driven Text-to-Image GenerationCode1
Knowledge-refined Denoising Network for Robust RecommendationCode1
Score-Based Diffusion Models as Principled Priors for Inverse ImagingCode1
DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic ModelCode1
The Devil is in the Upsampling: Architectural Decisions Made Simpler for Denoising with Deep Image PriorCode1
Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inferenceCode1
Automatically identifying ordinary differential equations from dataCode1
Cross-domain Denoising for Low-dose Multi-frame Spiral Computed TomographyCode1
Revisiting Implicit Neural Representations in Low-Level VisionCode1
Multi-scale Adaptive Fusion Network for Hyperspectral Image DenoisingCode1
A Comparison of Image Denoising MethodsCode1
Look ATME: The Discriminator Mean Entropy Needs AttentionCode1
DAS-N2N: Machine learning Distributed Acoustic Sensing (DAS) signal denoising without clean dataCode1
OVTrack: Open-Vocabulary Multiple Object TrackingCode1
Within-Camera Multilayer Perceptron DVS DenoisingCode1
DDT: Dual-branch Deformable Transformer for Image DenoisingCode1
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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