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 3201–3250 of 7282 papers

TitleStatusHype
Hypergraph Spectral Analysis and Processing in 3D Point Cloud—0
Geophysical inverse problems with measurement-guided diffusion models—0
Converting Anyone's Voice: End-to-End Expressive Voice Conversion with a Conditional Diffusion Model—0
Application of Spherical Convolutional Neural Networks to Image Reconstruction and Denoising in Nuclear Medicine—0
GETMusic: Generating Any Music Tracks with a Unified Representation and Diffusion Framework—0
An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning—0
Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models—0
A Second Order Cumulant Spectrum Test That a Stochastic Process is Strictly Stationary and a Step Toward a Test for Graph Signal Strict Stationarity—0
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
Equivariant plug-and-play image reconstruction—0
GLAMP: An Approximate Message Passing Framework for Transfer Learning with Applications to Lasso-based Estimators—0
Glauber Generative Model: Discrete Diffusion Models via Binary Classification—0
Global Context with Discrete Diffusion in Vector Quantised Modelling for Image Generation—0
Automatic Image Annotation via Label Transfer in the Semantic Space—0
Global hard thresholding algorithms for joint sparse image representation and denoising—0
Deep Image Destruction: Vulnerability of Deep Image-to-Image Models against Adversarial Attacks—0
Equivariant Denoisers for Image Restoration—0
Guided Motion Diffusion for Controllable Human Motion Synthesis—0
GM-LDM: Latent Diffusion Model for Brain Biomarker Identification through Functional Data-Driven Gray Matter Synthesis—0
Convergent regularization in inverse problems and linear plug-and-play denoisers—0
ASD-Diffusion: Anomalous Sound Detection with Diffusion Models—0
Deep Interaction between Masking and Mapping Targets for Single-Channel Speech Enhancement—0
Deep Interference Mitigation and Denoising of Real-World FMCW Radar Signals—0
GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models—0
Good Similar Patches for Image Denoising—0
Gotta Go Fast with Score-Based Generative Models—0
GPLD3D: Latent Diffusion of 3D Shape Generative Models by Enforcing Geometric and Physical Priors—0
EP-CFG: Energy-Preserving Classifier-Free Guidance—0
GPT-PPG: A GPT-based Foundation Model for Photoplethysmography Signals—0
GPU acceleration of NL-means, BM3D and VBM3D—0
多樣訊雜比之訓練語料於降噪自動編碼器其語音強化功能之初步研究 (A Preliminary Study of Various SNR-level Training Data in the Denoising Auto-encoder (DAE) Technique for Speech Enhancement) [In Chinese]—0
GradCheck: Analyzing classifier guidance gradients for conditional diffusion sampling—0
AdvLogo: Adversarial Patch Attack against Object Detectors based on Diffusion Models—0
Gradient-based Point Cloud Denoising with Uniformity—0
Gradient Descent for Deep Matrix Factorization: Dynamics and Implicit Bias towards Low Rank—0
Mjolnir: Breaking the Shield of Perturbation-Protected Gradients via Adaptive Diffusion—0
Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach—0
Gradient Domain Weighted Guided Image Filtering—0
Entropy stable conservative flux form neural networks—0
Convergence rates for pretraining and dropout: Guiding learning parameters using network structure—0
Gradient-Guided Conditional Diffusion Models for Private Image Reconstruction: Analyzing Adversarial Impacts of Differential Privacy and Denoising—0
Gradient Statistics Aware Power Control for Over-the-Air Federated Learning—0
Gradpaint: Gradient-Guided Inpainting with Diffusion Models—0
Gradual Training Method for Denoising Auto Encoders—0
Gradual training of deep denoising auto encoders—0
Graffe: Graph Representation Learning via Diffusion Probabilistic Models—0
Convergence of the denoising diffusion probabilistic models for general noise schedules—0
GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects—0
Graph-based denoising for time-varying point clouds—0
ENSURE: A General Approach for Unsupervised Training of Deep Image Reconstruction Algorithms—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