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 4451–4500 of 7282 papers

TitleStatusHype
Patient representation learning and interpretable evaluation using clinical notes—0
Pattern Denoising in Molecular Associative Memory using Pairwise Markov Random Field Models—0
PatternPaint: Practical Layout Pattern Generation Using Diffusion-Based Inpainting—0
Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models—0
PC2: Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction—0
PCB Defect Detection Using Denoising Convolutional Autoencoders—0
PCDNF: Revisiting Learning-based Point Cloud Denoising via Joint Normal Filtering—0
PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters—0
PCM : Picard Consistency Model for Fast Parallel Sampling of Diffusion Models—0
PCRDiffusion: Diffusion Probabilistic Models for Point Cloud Registration—0
PDFactor: Learning Tri-Perspective View Policy Diffusion Field for Multi-Task Robotic Manipulation—0
Ultrasound Speckle Suppression and Denoising using MRI-derived Normalizing Flow Priors—0
UltraVSR: Achieving Ultra-Realistic Video Super-Resolution with Efficient One-Step Diffusion Space—0
Unboxed: Geometrically and Temporally Consistent Video Outpainting—0
Perceptual-based deep-learning denoiser as a defense against adversarial attacks on ASR systems—0
Perceptual Kalman Filters: Online State Estimation under a Perfect Perceptual-Quality Constraint—0
Perceptually Optimized Generative Adversarial Network for Single Image Dehazing—0
AeGAN: Time-Frequency Speech Denoising via Generative Adversarial Networks—0
Performance Analysis of Plug-and-Play ADMM: A Graph Signal Processing Perspective—0
Performance Analysis of Spatial and Transform Filters for Efficient Image Noise Reduction—0
Performance characterization of a novel deep learning-based MR image reconstruction pipeline—0
Performance Limits of Stochastic Sub-Gradient Learning, Part I: Single Agent Case—0
PeriodGrad: Towards Pitch-Controllable Neural Vocoder Based on a Diffusion Probabilistic Model—0
Permuted and Unlinked Monotone Regression in R^d: an approach based on mixture modeling and optimal transport—0
Personalize Anything for Free with Diffusion Transformer—0
Personalized Face Inpainting with Diffusion Models by Parallel Visual Attention—0
Personalized Generative Low-light Image Denoising and Enhancement—0
Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification—0
Active Learning and Best-Response Dynamics—0
Perturbation theory approach to study the latent space degeneracy of Variational Autoencoders—0
PET image denoising based on denoising diffusion probabilistic models—0
PET Image Denoising via Text-Guided Diffusion: Integrating Anatomical Priors through Text Prompts—0
PEWA: Patch-based Exponentially Weighted Aggregation for image denoising—0
When Does Monolingual Data Help Multilingual Translation: The Role of Domain and Model Scale—0
Uncertainty-Aware Pedestrian Trajectory Prediction via Distributional Diffusion—0
pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning—0
PGDiffSeg: Prior-Guided Denoising Diffusion Model with Parameter-Shared Attention for Breast Cancer Segmentation—0
PGT-Net: Progressive Guided Multi-task Neural Network for Small-area Wet Fingerprint Denoising and Recognition—0
AliasNet: Alias Artefact Suppression Network for Accelerated Phase-Encode MRI—0
Phase Retrieval using Untrained Neural Network Priors—0
Phase retrieval with physics informed zero-shot learning—0
Phase Transitions in Image Denoising via Sparsely Coding Convolutional Neural Networks—0
P-HGRMS: A Parallel Hypergraph Based Root Mean Square Algorithm for Image Denoising—0
Phoenix: A Federated Generative Diffusion Model—0
Photon-counting CT using a Conditional Diffusion Model for Super-resolution and Texture-preservation—0
Phrase-Based \& Neural Unsupervised Machine Translation—0
PhysDiff: Physics-Guided Human Motion Diffusion Model—0
Physics-assisted Deep Learning for FMCW Radar Quantitative Imaging of Two-dimension Target—0
Physics-augmented Deep Learning with Adversarial Domain Adaptation: Applications to STM Image Denoising—0
Physics-aware Hand-object Interaction Denoising—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