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 4176–4200 of 7282 papers

TitleStatusHype
Noise-cleaning the precision matrix of fMRI time series—0
Adaptive Direction-Guided Structure Tensor Total Variation—0
Tree-based iterated local search for Markov random fields with applications in image analysis—0
Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation—0
TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models—0
Noise Distribution Adaptive Self-Supervised Image Denoising using Tweedie Distribution and Score Matching—0
Noise Estimation for Generative Diffusion Models—0
TriDo-Former: A Triple-Domain Transformer for Direct PET Reconstruction from Low-Dose Sinograms—0
Noise in Structured-Light Stereo Depth Cameras: Modeling and its Applications—0
Noise Learning Based Denoising Autoencoder—0
Noise-Level Estimation from Single Color Image Using Correlations Between Textures in RGB Channels—0
Adaptive diffusion constrained total variation scheme with application to `cartoon + texture + edge' image decomposition—0
Noise propagation and MP-PCA image denoising for high-resolution quantitative T2* and magnetic susceptibility mapping (QSM)—0
Noise Reconstruction and Removal Network: A New Way to Denoise FIB-SEM Images—0
Noise reduction in ISAR imaging of UAVs using weighted atomic norm minimization and 2D-ADMM algorithm—0
Noise Reduction to Compute Tissue Mineral Density and Trabecular Bone Volume Fraction from Low Resolution QCT—0
Noise-Robust Target-Speaker Voice Activity Detection Through Self-Supervised Pretraining—0
Noise-specific denoising method with applications to high-frequency ultrasonic images—0
Noise Synthesis for Low-Light Image Denoising with Diffusion Models—0
NoiseTrans: Point Cloud Denoising with Transformers—0
Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction—0
Non-asymptotic bounds for forward processes in denoising diffusions: Ornstein-Uhlenbeck is hard to beat—0
Broadening Target Distributions for Accelerated Diffusion Models via a Novel Analysis Approach—0
Non-autoregressive Conditional Diffusion Models for Time Series Prediction—0
Non-Autoregressive Diffusion-based Temporal Point Processes for Continuous-Time Long-Term Event Prediction—0
Show:102550
← PrevPage 168 of 292Next →
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