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 3476–3500 of 7282 papers

TitleStatusHype
Calliffusion: Chinese Calligraphy Generation and Style Transfer with Diffusion Modeling—0
Nested Diffusion Processes for Anytime Image GenerationCode1
Hybrid Driven Learning for Channel Estimation in Intelligent Reflecting Surface Aided Millimeter Wave Communications—0
HiFA: High-fidelity Text-to-3D Generation with Advanced Diffusion GuidanceCode2
Infrared Image Deturbulence Restoration Using Degradation Parameter-Assisted Wide & Deep LearningCode0
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold RobustnessCode0
Diffusion-Stego: Training-free Diffusion Generative Steganography via Message Projection—0
Diffusion Model for Dense MatchingCode1
On Diffusion Modeling for Anomaly DetectionCode1
Make-An-Audio 2: Temporal-Enhanced Text-to-Audio GenerationCode1
Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular DynamicsCode0
Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-DenoisingCode2
CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion ModelsCode1
Conditional score-based diffusion models for Bayesian inference in infinite dimensionsCode1
Observation Denoising in CYRUS Soccer Simulation 2D Team For RoboCup 2023Code1
On the Noise Sensitivity of the Randomized SVDCode0
A Diffusion Model for Event Skeleton GenerationCode0
Double Descent and Overfitting under Noisy Inputs and Distribution Shift for Linear Denoisers—0
Fast and Accurate Estimation of Low-Rank Matrices from Noisy Measurements via Preconditioned Non-Convex Gradient Descent—0
Error Bounds for Flow Matching Methods—0
Parallel Sampling of Diffusion ModelsCode1
Are Diffusion Models Vision-And-Language Reasoners?Code1
Knowledge Diffusion for DistillationCode1
NAP: Neural 3D Articulation PriorCode1
Weakly-Supervised Speech Pre-training: A Case Study on Target Speech Recognition—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