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 3376–3400 of 7282 papers

TitleStatusHype
A denoised Mean Teacher for domain adaptive point cloud registrationCode0
DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited DataCode0
A ground-based dataset and a diffusion model for on-orbit low-light image enhancement—0
Deep learning-based deconvolution for interferometric radio transient reconstructionCode0
Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image AttenuationCode1
ProRes: Exploring Degradation-aware Visual Prompt for Universal Image RestorationCode1
Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions—0
PromptIR: Prompting for All-in-One Blind Image RestorationCode2
Directional diffusion models for graph representation learning—0
Semi-Implicit Denoising Diffusion Models (SIDDMs)Code1
Resilient Sparse Array Radar with the Aid of Deep Learning—0
HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion Models—0
Masked Diffusion Models Are Fast Distribution LearnersCode1
EMoG: Synthesizing Emotive Co-speech 3D Gesture with Diffusion Model—0
Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct SupervisionCode1
Multi-Granularity Hand Action DetectionCode1
Conditional expectation using compactification operators—0
Weighted structure tensor total variation for image denoising—0
Efficient HDR Reconstruction from Real-World Raw Images—0
Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion ModelsCode1
Structural Restricted Boltzmann Machine for image denoising and classification—0
Drag-guided diffusion models for vehicle image generation—0
CANDID: Correspondence AligNment for Deep-burst Image Denoising—0
R2-Diff: Denoising by diffusion as a refinement of retrieved motion for image-based motion prediction—0
Diff-TTSG: Denoising probabilistic integrated speech and gesture synthesis—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