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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2976–3000 of 7282 papers

TitleStatusHype
Using Convolutional Neural Networks for Denoising and Deblending of Marine Seismic Data—0
Sub-graph Based Diffusion Model for Link Prediction—0
Pushing Joint Image Denoising and Classification to the Edge—0
Cross-conditioned Diffusion Model for Medical Image to Image Translation—0
MambaFoley: Foley Sound Generation using Selective State-Space ModelsCode0
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control—0
Quaternion Nuclear Norm minus Frobenius Norm Minimization for color image reconstruction—0
Critically Damped Third-Order Langevin Dynamics—0
Localized Schrödinger Bridge Sampler—0
LoRID: Low-Rank Iterative Diffusion for Adversarial Purification—0
MagicStyle: Portrait Stylization Based on Reference Image—0
Diffusion-Based Image-to-Image Translation by Noise Correction via Prompt Interpolation—0
FreeEnhance: Tuning-Free Image Enhancement via Content-Consistent Noising-and-Denoising Process—0
Exploring User-level Gradient Inversion with a Diffusion Prior—0
AdvLogo: Adversarial Patch Attack against Object Detectors based on Diffusion Models—0
ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals—0
Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models—0
Structure Modeling Activation Free Fourier Network for Spacecraft Image DenoisingCode0
Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learning—0
Constructing an Interpretable Deep Denoiser by Unrolling Graph Laplacian Regularizer—0
A Practical Gated Recurrent Transformer Network Incorporating Multiple Fusions for Video Denoising—0
CoDiCast: Conditional Diffusion Model for Global Weather Prediction with Uncertainty QuantificationCode0
pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning—0
STLLM-DF: A Spatial-Temporal Large Language Model with Diffusion for Enhanced Multi-Mode Traffic System Forecasting—0
Physics-augmented Deep Learning with Adversarial Domain Adaptation: Applications to STM Image 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