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 2851–2875 of 7282 papers

TitleStatusHype
Synthetic Augmentation for Anatomical Landmark Localization using DDPMs—0
Evaluating Utility of Memory Efficient Medical Image Generation: A Study on Lung Nodule Segmentation—0
MING: A Functional Approach to Learning Molecular Generative Models—0
Constrained Posterior Sampling: Time Series Generation with Hard Constraints—0
Learning Diffusion Model from Noisy Measurement using Principled Expectation-Maximization Method—0
From Real Artifacts to Virtual Reference: A Robust Framework for Translating Endoscopic Images—0
DDIL: Diversity Enhancing Diffusion Distillation With Imitation Learning—0
A Survey on Deep Tabular Learning—0
Fast Local Neural Regression for Low-Cost, Path Traced Lambertian Global Illumination—0
Rician Denoising Diffusion Probabilistic Models For Sodium Breast MRI Enhancement—0
DreamSteerer: Enhancing Source Image Conditioned Editability using Personalized Diffusion ModelsCode0
DRACO: A Denoising-Reconstruction Autoencoder for Cryo-EM—0
Feature-guided score diffusion for sampling conditional densities—0
MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling—0
Data-Aware Training Quality Monitoring and Certification for Reliable Deep LearningCode0
DMOSpeech: Direct Metric Optimization via Distilled Diffusion Model in Zero-Shot Speech Synthesis—0
Vision-guided and Mask-enhanced Adaptive Denoising for Prompt-based Image EditingCode0
Language Model Preference Evaluation with Multiple Weak EvaluatorsCode0
Physics-informed AI and ML-based sparse system identification algorithm for discovery of PDE's representing nonlinear dynamic systems—0
DuoDiff: Accelerating Diffusion Models with a Dual-Backbone ApproachCode0
Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities—0
Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction—0
Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning—0
GrabDAE: An Innovative Framework for Unsupervised Domain Adaptation Utilizing Grab-Mask and Denoise Auto-Encoder—0
DART: Denoising Autoregressive Transformer for Scalable Text-to-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