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 2226–2250 of 7282 papers

TitleStatusHype
Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-OnCode2
CM-TTS: Enhancing Real Time Text-to-Speech Synthesis Efficiency through Weighted Samplers and Consistency ModelsCode2
Denoising Low-dose Images Using Deep Learning of Time Series Images—0
Modeling State Shifting via Local-Global Distillation for Event-Frame Gaze TrackingCode1
GAMA-IR: Global Additive Multidimensional Averaging for Fast Image Restoration—0
IPT-V2: Efficient Image Processing Transformer using Hierarchical Attentions—0
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable DiffusionCode1
Generative AI Models for Different Steps in Architectural Design: A Literature Review—0
SGDFormer: One-stage Transformer-based Architecture for Cross-Spectral Stereo Image Guided Denoising—0
Denoising Monte Carlo Renders with Diffusion ModelsCode0
Binarized Low-light Raw Video EnhancementCode1
Structure Matters: Tackling the Semantic Discrepancy in Diffusion Models for Image InpaintingCode2
NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and DenoisingCode0
Diff-Reg v1: Diffusion Matching Model for Registration ProblemCode1
BAMM: Bidirectional Autoregressive Motion ModelCode2
DreamSalon: A Staged Diffusion Framework for Preserving Identity-Context in Editable Face Generation—0
Debiasing Cardiac Imaging with Controlled Latent Diffusion ModelsCode0
QNCD: Quantization Noise Correction for Diffusion ModelsCode0
ECNet: Effective Controllable Text-to-Image Diffusion Models—0
FlexEdit: Flexible and Controllable Diffusion-based Object-centric Image Editing—0
SingularTrajectory: Universal Trajectory Predictor Using Diffusion ModelCode2
LayoutFlow: Flow Matching for Layout Generation—0
Noise-Robust Keyword Spotting through Self-supervised PretrainingCode0
U-Sketch: An Efficient Approach for Sketch to Image Diffusion Models—0
Ship in Sight: Diffusion Models for Ship-Image Super ResolutionCode1
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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