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 1151–1175 of 7282 papers

TitleStatusHype
Discrete to Continuous: Generating Smooth Transition Poses from Sign Language Observation—0
Controllable Human Image Generation with Personalized Multi-Garments—0
Revisiting DDIM Inversion for Controlling Defect Generation by Disentangling the Background—0
MotionWavelet: Human Motion Prediction via Wavelet Manifold Learning—0
Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache SharingCode2
An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion ModelsCode2
DiffDesign: Controllable Diffusion with Meta Prior for Efficient Interior Design Generation—0
Comparison of Generative Learning Methods for Turbulence Modeling—0
PriorDiffusion: Leverage Language Prior in Diffusion Models for Monocular Depth Estimation—0
A Tunable Despeckling Neural Network Stabilized via Diffusion Equation—0
Making Images from Images: Interleaving Denoising and Transformation—0
Revelio: Interpreting and leveraging semantic information in diffusion modelsCode1
FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction—0
Classifier-Free Guidance inside the Attraction Basin May Cause MemorizationCode0
Haar-Laplacian for directed graphsCode0
Detecting Visual Triggers in Cannabis Imagery: A CLIP-Based Multi-Labeling Framework with Local-Global Aggregation—0
J-Invariant Volume Shuffle for Self-Supervised Cryo-Electron Tomogram Denoising on Single Noisy Volume—0
Foundation Cures Personalization: Recovering Facial Personalized Models' Prompt Consistency—0
DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous DrivingCode5
Prioritize Denoising Steps on Diffusion Model Preference Alignment via Explicit Denoised Distribution Estimation—0
High-Resolution Image Synthesis via Next-Token Prediction—0
Test-Time Adaptation of 3D Point Clouds via Denoising Diffusion Models—0
TaQ-DiT: Time-aware Quantization for Diffusion Transformers—0
Multitask Learning for SAR Ship Detection with Gaussian-Mask Joint Segmentation—0
Point Cloud Resampling with Learnable Heat Diffusion—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