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 4276–4300 of 7282 papers

TitleStatusHype
Boosting Fast and High-Quality Speech Synthesis with Linear Diffusion—0
Multi-Architecture Multi-Expert Diffusion Models—0
Convolutional Recurrent Neural Network with Attention for 3D Speech Enhancement—0
Complexity-aware Large Scale Origin-Destination Network Generation via Diffusion Model—0
BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping—0
SyncDiffusion: Coherent Montage via Synchronized Joint Diffusions—0
Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems—0
Non-autoregressive Conditional Diffusion Models for Time Series Prediction—0
Rethinking Weak Supervision in Helping Contrastive Learning—0
Phoenix: A Federated Generative Diffusion Model—0
DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT—0
RefineVIS: Video Instance Segmentation with Temporal Attention Refinement—0
Synthesizing realistic sand assemblies with denoising diffusion in latent space—0
PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation—0
Learning with Noisy Labels by Adaptive Gradient-Based Outlier RemovalCode0
GCD-DDPM: A Generative Change Detection Model Based on Difference-Feature Guided DDPMCode0
DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction—0
Compressed Sensing: A Discrete Optimization ApproachCode0
On the Behavior of Intrusive and Non-intrusive Speech Enhancement Metrics in Predictive and Generative Settings—0
Enhancing Point Annotations with Superpixel and Confidence Learning Guided for Improving Semi-Supervised OCT Fluid Segmentation—0
Transformer-Based UNet with Multi-Headed Cross-Attention Skip Connections to Eliminate Artifacts in Scanned Documents—0
Perceptual Kalman Filters: Online State Estimation under a Perfect Perceptual-Quality Constraint—0
DiffECG: A Versatile Probabilistic Diffusion Model for ECG Signals Synthesis—0
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation—0
Denoising Diffusion Semantic Segmentation with Mask Prior Modeling—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