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 5826–5850 of 7282 papers

TitleStatusHype
SPADE: Spectroscopic Photoacoustic Denoising using an Analytical and Data-free Enhancement Framework—0
Contrastive Learning for Low-light Raw Denoising—0
Contrastive Multi-Modal Representation Learning for Spark Plug Fault Diagnosis—0
Contrast-Unity for Partially-Supervised Temporal Sentence Grounding—0
Controllable Confidence-Based Image Denoising—0
Controllable Diverse Sampling for Diffusion Based Motion Behavior Forecasting—0
AnaMoDiff: 2D Analogical Motion Diffusion via Disentangled Denoising—0
Controllable Human Image Generation with Personalized Multi-Garments—0
Controllable Inversion of Black-Box Face Recognition Models via Diffusion—0
Controllable Person Image Synthesis with Pose-Constrained Latent Diffusion—0
SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries—0
Controllable Satellite-to-Street-View Synthesis with Precise Pose Alignment and Zero-Shot Environmental Control—0
Controlled and Conditional Text to Image Generation with Diffusion Prior—0
Controlled Learning of Pointwise Nonlinearities in Neural-Network-Like Architectures—0
Controlling Ensemble Variance in Diffusion Models: An Application for Reanalyses Downscaling—0
SparGE: Sparse Coding-based Patient Similarity Learning via Low-rank Constraints and Graph Embedding—0
ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model—0
Convergence Analysis of a Proximal Stochastic Denoising Regularization Algorithm—0
Spark Deficient Gabor Frames for Inverse Problems—0
Convergence of denoising diffusion models under the manifold hypothesis—0
Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions—0
Convergence of gradient based pre-training in Denoising autoencoders—0
Convergence of score-based generative modeling for general data distributions—0
Convergence of the denoising diffusion probabilistic models for general noise schedules—0
Convergence rates for pretraining and dropout: Guiding learning parameters using network structure—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