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 2051–2075 of 7282 papers

TitleStatusHype
Prompt Mixing in Diffusion Models using the Black Scholes AlgorithmCode0
Adaptive Bayesian Multivariate Spline Knot Inference with Prior Specifications on Model ComplexityCode0
MetaEarth: A Generative Foundation Model for Global-Scale Remote Sensing Image Generation—0
Score-based Generative Models with Adaptive Momentum—0
Conditioning diffusion models by explicit forward-backward bridgingCode0
Matrix Denoising with Doubly Heteroscedastic Noise: Fundamental Limits and Optimal Spectral Methods—0
DiffNorm: Self-Supervised Normalization for Non-autoregressive Speech-to-speech TranslationCode0
Hybrid Digital-Analog Semantic Communications—0
DARK: Denoising, Amplification, Restoration KitCode0
Kernel spectral joint embeddings for high-dimensional noisy datasets using duo-landmark integral operators—0
Physics-aware Hand-object Interaction Denoising—0
MAMCA -- Optimal on Accuracy and Efficiency for Automatic Modulation Classification with Extended Signal LengthCode2
Dreamer XL: Towards High-Resolution Text-to-3D Generation via Trajectory Score MatchingCode2
On the Trajectory Regularity of ODE-based Diffusion SamplingCode3
Double Correction Framework for Denoising RecommendationCode0
TexPainter: Generative Mesh Texturing with Multi-view ConsistencyCode2
LighTDiff: Surgical Endoscopic Image Low-Light Enhancement with T-DiffusionCode1
TVCondNet: A Conditional Denoising Neural Network for NMR Spectroscopy—0
Dual3D: Efficient and Consistent Text-to-3D Generation with Dual-mode Multi-view Latent Diffusion—0
VisioBlend: Sketch and Stroke-Guided Denoising Diffusion Probabilistic Model for Realistic Image Generation—0
UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning—0
Response Matching for generating materials and molecules—0
RSHazeDiff: A Unified Fourier-aware Diffusion Model for Remote Sensing Image DehazingCode1
QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models—0
UnMarker: A Universal Attack on Defensive Image WatermarkingCode3
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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