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 2651–2700 of 7282 papers

TitleStatusHype
3D Wasserstein generative adversarial network with dense U-Net based discriminator for preclinical fMRI denoising—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
FiRe: Fixed-points of Restoration Priors for Solving Inverse ProblemsCode0
Towards Chunk-Wise Generation for Long Videos—0
Limit Order Book Event Stream Prediction with Diffusion Model—0
Unpacking the Individual Components of Diffusion Policy—0
Understanding Galaxy Morphology Evolution Through Cosmic Time via Redshift Conditioned Diffusion ModelsCode0
Prediction with Action: Visual Policy Learning via Joint Denoising Process—0
Steering Rectified Flow Models in the Vector Field for Controlled Image Generation—0
SuperMat: Physically Consistent PBR Material Estimation at Interactive Rates—0
Contrastive CFG: Improving CFG in Diffusion Models by Contrasting Positive and Negative Concepts—0
Mixed-State Quantum Denoising Diffusion Probabilistic Model—0
Optimal Estimation of Shared Singular Subspaces across Multiple Noisy Matrices—0
Reward Incremental Learning in Text-to-Image Generation—0
DiffDesign: Controllable Diffusion with Meta Prior for Efficient Interior Design Generation—0
Revisiting DDIM Inversion for Controlling Defect Generation by Disentangling the Background—0
MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing—0
MotionWavelet: Human Motion Prediction via Wavelet Manifold Learning—0
Controllable Human Image Generation with Personalized Multi-Garments—0
NovelGS: Consistent Novel-view Denoising via Large Gaussian Reconstruction Model—0
Comparison of Generative Learning Methods for Turbulence Modeling—0
Discrete to Continuous: Generating Smooth Transition Poses from Sign Language Observation—0
Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory—0
A Tunable Despeckling Neural Network Stabilized via Diffusion Equation—0
Making Images from Images: Interleaving Denoising and Transformation—0
PriorDiffusion: Leverage Language Prior in Diffusion Models for Monocular Depth Estimation—0
FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction—0
Classifier-Free Guidance inside the Attraction Basin May Cause MemorizationCode0
Haar-Laplacian for directed graphsCode0
Detecting Visual Triggers in Cannabis Imagery: A CLIP-Based Multi-Labeling Framework with Local-Global Aggregation—0
Foundation Cures Personalization: Recovering Facial Personalized Models' Prompt Consistency—0
J-Invariant Volume Shuffle for Self-Supervised Cryo-Electron Tomogram Denoising on Single Noisy Volume—0
High-Resolution Image Synthesis via Next-Token Prediction—0
Prioritize Denoising Steps on Diffusion Model Preference Alignment via Explicit Denoised Distribution Estimation—0
Point Cloud Denoising With Fine-Granularity Dynamic Graph Convolutional Networks—0
TaQ-DiT: Time-aware Quantization for Diffusion Transformers—0
Enhancing Medical Image Segmentation with Deep Learning and Diffusion Models—0
Test-Time Adaptation of 3D Point Clouds via Denoising Diffusion Models—0
Point Cloud Resampling with Learnable Heat Diffusion—0
Novel View Extrapolation with Video Diffusion Priors—0
Multitask Learning for SAR Ship Detection with Gaussian-Mask Joint Segmentation—0
Analysis and Synthesis Denoisers for Forward-Backward Plug-and-Play Algorithms—0
DAGSM: Disentangled Avatar Generation with GS-enhanced Mesh—0
EEG Signal Denoising Using pix2pix GAN: Enhancing Neurological Data Analysis—0
A Neural Denoising Vocoder for Clean Waveform Generation from Noisy Mel-Spectrogram based on Amplitude and Phase Predictions—0
A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant informationCode0
mDAE : modified Denoising AutoEncoder for missing data imputation—0
Frequency-Aware Guidance for Blind Image Restoration via Diffusion Models—0
Robust multi-coil MRI reconstruction via self-supervised denoisingCode0
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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