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 3026–3050 of 7282 papers

TitleStatusHype
RLCP: A Reinforcement Learning-based Copyright Protection Method for Text-to-Image Diffusion Model—0
A Deep-Learning-Based Label-free No-Reference Image Quality Assessment Metric: Application in Sodium MRI Denoising—0
Meta-Learn Unimodal Signals with Weak Supervision for Multimodal Sentiment Analysis—0
Airfoil Diffusion: Denoising Diffusion Model For Conditional Airfoil GenerationCode0
Single Image Denoising via a New Lightweight Learning-Based ModelCode0
ConsistencyTrack: A Robust Multi-Object Tracker with a Generation Strategy of Consistency ModelCode0
A Novel Denoising Technique and Deep Learning Based Hybrid Wind Speed Forecasting Model for Variable Terrain Conditions—0
Sigma Flows for Image and Data Labeling and Learning Structured Prediction—0
MeshUp: Multi-Target Mesh Deformation via Blended Score Distillation—0
CrossViewDiff: A Cross-View Diffusion Model for Satellite-to-Street View Synthesis—0
A Preliminary Exploration Towards General Image Restoration—0
DiffSurf: A Transformer-based Diffusion Model for Generating and Reconstructing 3D Surfaces in Pose—0
A Multiscale Gradient Fusion Method for Edge Detection in Color Images Utilizing the CBM3D Filter—0
Foodfusion: A Novel Approach for Food Image Composition via Diffusion Models—0
TC-PDM: Temporally Consistent Patch Diffusion Models for Infrared-to-Visible Video TranslationCode0
Bring the Power of Diffusion Model to Defect Detection—0
Non-asymptotic bounds for forward processes in denoising diffusions: Ornstein-Uhlenbeck is hard to beat—0
Decoupled Video Generation with Chain of Training-free Diffusion Model Experts—0
PointDGMamba: Domain Generalization of Point Cloud Classification via Generalized State Space ModelCode0
How Diffusion Models Learn to Factorize and Compose—0
General Intelligent Imaging and Uncertainty Quantification by Deterministic Diffusion Model—0
Controlled Learning of Pointwise Nonlinearities in Neural-Network-Like Architectures—0
IFH: a Diffusion Framework for Flexible Design of Graph Generative ModelsCode0
Variance reduction of diffusion model's gradients with Taylor approximation-based control variate—0
Adapting MIMO video restoration networks to low latency constraints—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