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 2426–2450 of 7282 papers

TitleStatusHype
Watermarking in Diffusion Model: Gaussian Shading with Exact Diffusion Inversion via Coupled Transformations (EDICT)—0
Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisition—0
Robust Low-Light Human Pose Estimation through Illumination-Texture Modulation—0
Cloud Removal With PolSAR-Optical Data Fusion Using A Two-Flow Residual Network—0
Generative AI Enabled Robust Sensor Placement in Cyber-Physical Power Systems: A Graph Diffusion Approach—0
ODPG: Outfitting Diffusion with Pose Guided Condition—0
Comparison of Autoencoders for tokenization of ASL datasets—0
Wavelet Integrated Convolutional Neural Network for ECG Signal Denoising—0
Quantum Annealing for Robust Principal Component AnalysisCode0
LLVD: LSTM-based Explicit Motion Modeling in Latent Space for Blind Video Denoising—0
Towards Balanced Continual Multi-Modal Learning in Human Pose Estimation—0
Targeted Adversarial Denoising Autoencoders (TADA) for Neural Time Series Filtration—0
Tuning-Free Long Video Generation via Global-Local Collaborative Diffusion—0
Geophysical inverse problems with measurement-guided diffusion models—0
Color Correction Meets Cross-Spectral Refinement: A Distribution-Aware Diffusion for Underwater Image Restoration—0
Exploring Optimal Latent Trajetory for Zero-shot Image Editing—0
Exploring Molecule Generation Using Latent Space Graph DiffusionCode0
Brick-Diffusion: Generating Long Videos with Brick-to-Wall Denoising—0
InpDiffusion: Image Inpainting Localization via Conditional Diffusion Models—0
Two-Dimensional Unknown View Tomography from Unknown Angle Distributions—0
DDRM-PR: Fourier Phase Retrieval using Denoising Diffusion Restoration Models—0
DarkFarseer: Inductive Spatio-temporal Kriging via Hidden Style Enhancement and Sparsity-Noise Mitigation—0
A Self-supervised Diffusion Bridge for MRI Reconstruction—0
Noise-Robust Target-Speaker Voice Activity Detection Through Self-Supervised Pretraining—0
DeTrack: In-model Latent Denoising Learning for Visual Object Tracking—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