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 3276–3300 of 7282 papers

TitleStatusHype
A Theoretically Guaranteed Quaternion Weighted Schatten p-norm Minimization Method for Color Image RestorationCode0
A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis—0
A Simple and Model-Free Path Filtering Algorithm for Smoothing and AccuracyCode2
DiAMoNDBack: Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein TracesCode1
ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT DenoisingCode1
ResWCAE: Biometric Pattern Image Denoising Using Residual Wavelet-Conditioned Autoencoder—0
InFusion: Inject and Attention Fusion for Multi Concept Zero-Shot Text-based Video Editing—0
PartDiff: Image Super-resolution with Partial Diffusion Models—0
FEDD -- Fair, Efficient, and Diverse Diffusion-based Lesion Segmentation and Malignancy ClassificationCode1
DPM-OT: A New Diffusion Probabilistic Model Based on Optimal TransportCode1
BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained DiffusionCode2
Self2Self+: Single-Image Denoising with Self-Supervised Learning and Image Quality Assessment LossCode1
Dehazing Ultrasound using Diffusion Models—0
Fractional Denoising for 3D Molecular Pre-trainingCode1
Risk-optimized Outlier Removal for Robust 3D Point Cloud ClassificationCode1
Deep Multi-Threshold Spiking-UNet for Image ProcessingCode1
AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic ModelsCode1
PreDiff: Precipitation Nowcasting with Latent Diffusion ModelsCode1
BSDM: Background Suppression Diffusion Model for Hyperspectral Anomaly DetectionCode1
Generative Prompt Model for Weakly Supervised Object LocalizationCode1
Alioth: A Machine Learning Based Interference-Aware Performance Monitor for Multi-Tenancy Applications in Public CloudCode0
Radar-STDA: A High-Performance Spatial-Temporal Denoising Autoencoder for Interference Mitigation of FMCW RadarsCode1
Convergent regularization in inverse problems and linear plug-and-play denoisers—0
Towards Authentic Face Restoration with Iterative Diffusion Models and Beyond—0
Soft-IntroVAE for Continuous Latent space Image Super-Resolution—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