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 3201–3225 of 7282 papers

TitleStatusHype
Unsupervised Image Denoising in Real-World Scenarios via Self-Collaboration Parallel Generative Adversarial BranchesCode1
Accelerating Diffusion-based Combinatorial Optimization Solvers by Progressive Distillation—0
Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh ReconstructionCode1
ModelScope Text-to-Video Technical ReportCode3
Taming the Power of Diffusion Models for High-Quality Virtual Try-On with Appearance FlowCode2
Illumination and Shadows in Head Rotation: experiments with Denoising Diffusion Models—0
TriDo-Former: A Triple-Domain Transformer for Direct PET Reconstruction from Low-Dose Sinograms—0
Masked Diffusion as Self-supervised Representation LearnerCode1
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE SolversCode1
Optimizing a Transformer-based network for a deep learning seismic processing workflow—0
On Error Propagation of Diffusion Models—0
Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image DenoisingCode1
Deep Learning Architecture for Motor Imaged Words—0
Target Speech Extraction with Conditional Diffusion Model—0
3D Scene Diffusion Guidance using Scene Graphs—0
Evaluation of a Low-Cost Single-Lead ECG Module for Vascular Ageing Prediction and Studying Smoking-induced Changes in ECG—0
Assessing Adversarial Replay and Deep Learning-Driven Attacks on Specific Emitter Identification-based Security Approaches—0
Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise ModelCode2
Nearly d-Linear Convergence Bounds for Diffusion Models via Stochastic Localization—0
Recurrent Self-Supervised Video Denoising with Denser Receptive FieldCode0
All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation—0
Unfolded proximal neural networks for robust image Gaussian denoising—0
NNVISR: Bring Neural Network Video Interpolation and Super Resolution into Video Processing FrameworkCode1
Recurrent Spike-based Image Restoration under General IlluminationCode0
Non-line-of-sight reconstruction via structure sparsity regularization—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