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 601–625 of 7282 papers

TitleStatusHype
SymmetricDiffusers: Learning Discrete Diffusion on Finite Symmetric GroupsCode1
HarmoniCa: Harmonizing Training and Inference for Better Feature Caching in Diffusion Transformer AccelerationCode1
Edge-preserving noise for diffusion modelsCode1
A Cat Is A Cat (Not A Dog!): Unraveling Information Mix-ups in Text-to-Image Encoders through Causal Analysis and Embedding OptimizationCode1
Volumetric Conditional Score-based Residual Diffusion Model for PET/MR DenoisingCode1
Vision-Language Models are Strong Noisy Label DetectorsCode1
MCDDPM: Multichannel Conditional Denoising Diffusion Model for Unsupervised Anomaly Detection in Brain MRICode1
Underwater Image Enhancement with Physical-based Denoising Diffusion Implicit ModelsCode1
MECG-E: Mamba-based ECG Enhancer for Baseline Wander RemovalCode1
Neural Differential Appearance EquationsCode1
DenoMamba: A fused state-space model for low-dose CT denoisingCode1
Massively Multi-Person 3D Human Motion Forecasting with Scene ContextCode1
Temporal As a Plugin: Unsupervised Video Denoising with Pre-Trained Image DenoisersCode1
Ultrasound Image Enhancement with the Variance of Diffusion ModelsCode1
SteeredMarigold: Steering Diffusion Towards Depth Completion of Largely Incomplete Depth MapsCode1
Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug DesignCode1
DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-DrivingCode1
xTED: Cross-Domain Adaptation via Diffusion-Based Trajectory EditingCode1
Estimating Atmospheric Variables from Digital Typhoon Satellite Images via Conditional Denoising Diffusion ModelsCode1
Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image EnhancementCode1
DiffQRCoder: Diffusion-based Aesthetic QR Code Generation with Scanning Robustness Guided Iterative RefinementCode1
Dual convolutional neural network with attention for image blind denoisingCode1
DKDM: Data-Free Knowledge Distillation for Diffusion Models with Any ArchitectureCode1
HiPrompt: Tuning-free Higher-Resolution Generation with Hierarchical MLLM PromptsCode1
Diffusion Models Learn Low-Dimensional Distributions via Subspace ClusteringCode1
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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