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 1101–1125 of 7282 papers

TitleStatusHype
Diffusion Implicit Policy for Unpaired Scene-aware Motion Synthesis—0
OmniCreator: Self-Supervised Unified Generation with Universal Editing—0
On the Feature Learning in Diffusion Models—0
Concept Replacer: Replacing Sensitive Concepts in Diffusion Models via Precision LocalizationCode0
NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic Adversarial Training—0
An overview of diffusion models for generative artificial intelligence—0
Schedule On the Fly: Diffusion Time Prediction for Faster and Better Image Generation—0
MFTF: Mask-free Training-free Object Level Layout Control Diffusion ModelCode0
A Lesson in Splats: Teacher-Guided Diffusion for 3D Gaussian Splats Generation with 2D Supervision—0
Advanced Video Inpainting Using Optical Flow-Guided Efficient DiffusionCode3
Efficient Off-Grid Bayesian Parameter Estimation for Kronecker-Structured SignalsCode0
DogLayout: Denoising Diffusion GAN for Discrete and Continuous Layout GenerationCode1
Diffusion Model Guided Sampling with Pixel-Wise Aleatoric Uncertainty EstimationCode1
Contextual Checkerboard Denoise -- A Novel Neural Network-Based Approach for Classification-Aware OCT Image DenoisingCode0
MoTe: Learning Motion-Text Diffusion Model for Multiple Generation Tasks—0
TexGaussian: Generating High-quality PBR Material via Octree-based 3D Gaussian SplattingCode2
Riemannian Denoising Score Matching for Molecular Structure Optimization with Accurate Energy—0
Diffusion Models Meet Network Management: Improving Traffic Matrix Analysis with Diffusion-based ApproachCode0
FiRe: Fixed-points of Restoration Priors for Solving Inverse ProblemsCode0
MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient NetworkCode0
Timestep Embedding Tells: It's Time to Cache for Video Diffusion ModelCode1
Z-STAR+: A Zero-shot Style Transfer Method via Adjusting Style Distribution—0
3D Wasserstein generative adversarial network with dense U-Net based discriminator for preclinical fMRI denoising—0
Data Augmentation with Diffusion Models for Colon Polyp Localization on the Low Data Regime: How much real data is enough?—0
Towards a Mechanistic Explanation of Diffusion Model Generalization—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