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 2626–2650 of 7282 papers

TitleStatusHype
Robust Image Denoising through Adversarial Frequency MixupCode1
Dual Prior Unfolding for Snapshot Compressive ImagingCode1
CoDe: An Explicit Content Decoupling Framework for Image Restoration—0
DiffusionTrack: Point Set Diffusion Model for Visual Object TrackingCode0
Real-World Mobile Image Denoising Dataset with Efficient BaselinesCode2
Towards More Accurate Diffusion Model Acceleration with A Timestep TunerCode0
Exposure Bracketing Is All You Need For A High-Quality ImageCode2
Revisiting Nonlocal Self-Similarity from Continuous Representation—0
Depth Map Denoising Network and Lightweight Fusion Network for Enhanced 3D Face Recognition—0
Hyperspectral Image Denoising via Spatial-Spectral Recurrent TransformerCode0
UGPNet: Universal Generative Prior for Image Restoration—0
SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries—0
Diff-PCR: Diffusion-Based Correspondence Searching in Doubly Stochastic Matrix Space for Point Cloud Registration—0
Diffusion Model with Perceptual Loss—0
Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion ModelsCode1
6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimation—0
Improving Image Restoration through Removing Degradations in Textual RepresentationsCode1
Learning the Dynamic Correlations and Mitigating Noise by Hierarchical Convolution for Long-term Sequence ForecastingCode0
Grounding-Prompter: Prompting LLM with Multimodal Information for Temporal Sentence Grounding in Long Videos—0
Restoration by Generation with Constrained Priors—0
Natural Adversarial Patch Generation Method Based on Latent Diffusion Model—0
Self-supervised Pretraining for Robust Personalized Voice Activity Detection in Adverse Conditions—0
Image Restoration by Denoising Diffusion Models with Iteratively Preconditioned GuidanceCode1
Learn From Orientation Prior for Radiograph Super-Resolution: Orientation Operator Transformer—0
PanGu-Draw: Advancing Resource-Efficient Text-to-Image Synthesis with Time-Decoupled Training and Reusable Coop-Diffusion—0
Show:102550
← PrevPage 106 of 292Next →
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