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 801–825 of 7282 papers

TitleStatusHype
Learning a Diffusion Model Policy from Rewards via Q-Score MatchingCode1
NM-FlowGAN: Modeling sRGB Noise without Paired Images using a Hybrid Approach of Normalizing Flows and GANCode1
PPFM: Image denoising in photon-counting CT using single-step posterior sampling Poisson flow generative modelsCode1
Focus on Your Instruction: Fine-grained and Multi-instruction Image Editing by Attention ModulationCode1
Efficient and Scalable Graph Generation through Iterative Local ExpansionCode1
LatentEditor: Text Driven Local Editing of 3D ScenesCode1
World Models via Policy-Guided Trajectory DiffusionCode1
ERASE: Error-Resilient Representation Learning on Graphs for Label Noise ToleranceCode1
SimAC: A Simple Anti-Customization Method for Protecting Face Privacy against Text-to-Image Synthesis of Diffusion ModelsCode1
Image is All You Need to Empower Large-scale Diffusion Models for In-Domain GenerationCode1
Clockwork Diffusion: Efficient Generation With Model-Step DistillationCode1
The Lottery Ticket Hypothesis in Denoising: Towards Semantic-Driven InitializationCode1
Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-ExpertsCode1
Optimized View and Geometry Distillation from Multi-view DiffuserCode1
Characteristic Guidance: Non-linear Correction for Diffusion Model at Large Guidance ScaleCode1
The Journey, Not the Destination: How Data Guides Diffusion ModelsCode1
Textual Prompt Guided Image RestorationCode1
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy LabelsCode1
MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion AttacksCode1
D3A-TS: Denoising-Driven Data Augmentation in Time SeriesCode1
Uncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic ModelsCode1
Prompt-In-Prompt Learning for Universal Image RestorationCode1
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic ModelsCode1
EulerMormer: Robust Eulerian Motion Magnification via Dynamic Filtering within TransformerCode1
PrimDiffusion: Volumetric Primitives Diffusion for 3D Human GenerationCode1
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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