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 5001–5025 of 7282 papers

TitleStatusHype
X-Adapter: Adding Universal Compatibility of Plugins for Upgraded Diffusion Model—0
S2GO: Streaming Sparse Gaussian Occupancy Prediction—0
S2S-WTV: Seismic Data Noise Attenuation Using Weighted Total Variation Regularized Self-Supervised Learning—0
S5 Framework: A Review of Self-Supervised Shared Semantic Space Optimization for Multimodal Zero-Shot Learning—0
Unsupervised patient representations from clinical notes with interpretable classification decisions—0
Saddlepoints in Unsupervised Least Squares—0
Accelerating Image Generation with Sub-path Linear Approximation Model—0
SafeDiffuser: Safe Planning with Diffusion Probabilistic Models—0
Safety-Critical Traffic Simulation with Guided Latent Diffusion Model—0
Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100 speed-up—0
SAIL: Search-Augmented Instruction Learning—0
Accelerating Diffusion Sampling via Exploiting Local Transition Coherence—0
Salient Conditional Diffusion for Defending Against Backdoor Attacks—0
Salt and pepper noise removal method based on stationary Framelet transform with non-convex sparsity regularization—0
Only Relevant Information Matters: Filtering Out Noisy Samples to Boost RL—0
Samples Are Useful? Not Always: denoising policy gradient updates using variance explained—0
Sampling Based Scene-Space Video Processing—0
Sampling, Diffusions, and Stochastic Localization—0
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions—0
Generative diffusion for perceptron problems: statistical physics analysis and efficient algorithms—0
Sapinet: A sparse event-based spatiotemporal oscillator for learning in the wild—0
SAR2EO: A High-resolution Image Translation Framework with Denoising Enhancement—0
Unsupervised PET Reconstruction from a Bayesian Perspective—0
SAR Despeckling via Regional Denoising Diffusion Probabilistic Model—0
SAR Image Despeckling Based on Convolutional Denoising Autoencoder—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