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 2626–2650 of 7282 papers

TitleStatusHype
Imagine360: Immersive 360 Video Generation from Perspective Anchor—0
End-to-end Triple-domain PET Enhancement: A Hybrid Denoising-and-reconstruction Framework for Reconstructing Standard-dose PET Images from Low-dose PET Sinograms—0
UTSD: Unified Time Series Diffusion Model—0
Partially Conditioned Patch Parallelism for Accelerated Diffusion Model Inference—0
Grayscale to Hyperspectral at Any Resolution Using a Phase-Only Lens—0
OmniCreator: Self-Supervised Unified Generation with Universal Editing—0
Diffusion Implicit Policy for Unpaired Scene-aware Motion Synthesis—0
MFTF: Mask-free Training-free Object Level Layout Control Diffusion ModelCode0
Schedule On the Fly: Diffusion Time Prediction for Faster and Better Image Generation—0
NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic Adversarial Training—0
Concept Replacer: Replacing Sensitive Concepts in Diffusion Models via Precision LocalizationCode0
An overview of diffusion models for generative artificial intelligence—0
On the Feature Learning in Diffusion Models—0
A Lesson in Splats: Teacher-Guided Diffusion for 3D Gaussian Splats Generation with 2D Supervision—0
Efficient Off-Grid Bayesian Parameter Estimation for Kronecker-Structured SignalsCode0
Riemannian Denoising Score Matching for Molecular Structure Optimization with Accurate Energy—0
MoTe: Learning Motion-Text Diffusion Model for Multiple Generation Tasks—0
Contextual Checkerboard Denoise -- A Novel Neural Network-Based Approach for Classification-Aware OCT Image DenoisingCode0
Diffusion Models Meet Network Management: Improving Traffic Matrix Analysis with Diffusion-based ApproachCode0
MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient NetworkCode0
FiRe: Fixed-points of Restoration Priors for Solving Inverse ProblemsCode0
VIPaint: Image Inpainting with Pre-Trained Diffusion Models via Variational Inference—0
Data Augmentation with Diffusion Models for Colon Polyp Localization on the Low Data Regime: How much real data is enough?—0
Structured Object Language Modeling (SoLM): Native Structured Objects Generation Conforming to Complex Schemas with Self-Supervised Denoising—0
SOWing Information: Cultivating Contextual Coherence with MLLMs in Image Generation—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