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 3626–3650 of 7282 papers

TitleStatusHype
Non-Contact Heart Rate Measurement from Deteriorated Videos—0
Deep sound-field denoiser: optically-measured sound-field denoising using deep neural networkCode0
Single-View Height Estimation with Conditional Diffusion Probabilistic Models—0
MRI Recovery with Self-Calibrated Denoisers without Fully-Sampled DataCode0
The Score-Difference Flow for Implicit Generative Modeling—0
NoiseTrans: Point Cloud Denoising with Transformers—0
DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic ModelCode1
Score-Based Diffusion Models as Principled Priors for Inverse ImagingCode1
The Devil is in the Upsampling: Architectural Decisions Made Simpler for Denoising with Deep Image PriorCode1
Conditional Denoising Diffusion for Sequential Recommendation—0
Fast Diffusion Probabilistic Model Sampling through the lens of Backward Error Analysis—0
Automatically identifying ordinary differential equations from dataCode1
Heart Rate Extraction from Abdominal Audio Signals—0
Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inferenceCode1
Cross-domain Denoising for Low-dose Multi-frame Spiral Computed TomographyCode1
H2TF for Hyperspectral Image Denoising: Where Hierarchical Nonlinear Transform Meets Hierarchical Matrix Factorization—0
An Attention Free Conditional Autoencoder For Anomaly Detection in Cryptocurrencies—0
Revisiting Implicit Neural Representations in Low-Level VisionCode1
Collaborative Diffusion for Multi-Modal Face Generation and EditingCode2
Denoising Cosine Similarity: A Theory-Driven Approach for Efficient Representation Learning—0
Self-supervised Image Denoising with Downsampled Invariance Loss and Conditional Blind-Spot Network—0
Multi-scale Adaptive Fusion Network for Hyperspectral Image DenoisingCode1
Denoising Diffusion Medical Models—0
Look ATME: The Discriminator Mean Entropy Needs AttentionCode1
A Comparison of Image Denoising MethodsCode1
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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