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 4576–4600 of 7282 papers

TitleStatusHype
Salient Conditional Diffusion for Defending Against Backdoor Attacks—0
Zero-shot-Learning Cross-Modality Data Translation Through Mutual Information Guided Stochastic Diffusion—0
Hierarchical Disentangled Representation for Invertible Image Denoising and Beyond—0
NoiseTransfer: Image Noise Generation with Contrastive EmbeddingsCode0
ERA-Solver: Error-Robust Adams Solver for Fast Sampling of Diffusion Probabilistic Models—0
Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Data—0
Diffusion Denoising for Low-Dose-CT Model—0
A Denoising Diffusion Model for Fluid Field Prediction—0
On the Importance of Noise Scheduling for Diffusion ModelsCode0
Dual Diffusion Architecture for Fisheye Image Rectification: Synthetic-to-Real Generalization—0
Deep Convolutional Framelet Denoising for Panoramic by Mixed Wavelet Integration—0
DiffMotion: Speech-Driven Gesture Synthesis Using Denoising Diffusion ModelCode0
A Semantic Modular Framework for Events Topic Modeling in Social Media—0
Recent progress in image denoising: A training strategy perspective—0
Regular Time-series Generation using SGM—0
Impact of PCA-based preprocessing and different CNN structures on deformable registration of sonograms—0
Dif-Fusion: Towards High Color Fidelity in Infrared and Visible Image Fusion with Diffusion Models—0
Denoising Diffusion Probabilistic Models as a Defense against Adversarial AttacksCode0
Online Filtering over Expanding Graphs—0
FEUNet: a flexible and effective U-shaped network for image denoisingCode0
NCP: Neural Correspondence Prior for Effective Unsupervised Shape MatchingCode0
DINF: Dynamic Instance Noise Filter for Occluded Pedestrian Detection—0
Thompson Sampling with Diffusion Generative Prior—0
A Possible Converter to Denoise the Images of Exoplanet Candidates through Machine Learning Techniques—0
Speech Driven Video Editing via an Audio-Conditioned Diffusion Model—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