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 3726–3750 of 7282 papers

TitleStatusHype
DiffForensics: Leveraging Diffusion Prior to Image Forgery Detection and Localization—0
Depth Map Denoising Network and Lightweight Fusion Network for Enhanced 3D Face Recognition—0
Hyper-MD: Mesh Denoising with Customized Parameters Aware of Noise Intensity and Geometric Characteristics—0
Slice3D: Multi-Slice Occlusion-Revealing Single View 3D Reconstruction—0
Unmixing Diffusion for Self-Supervised Hyperspectral Image Denoising—0
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
Hyperspectral Image Denoising via Spatial-Spectral Recurrent TransformerCode0
Diffusion Model with Perceptual Loss—0
6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimation—0
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
PanGu-Draw: Advancing Resource-Efficient Text-to-Image Synthesis with Time-Decoupled Training and Reusable Coop-Diffusion—0
Learn From Orientation Prior for Radiograph Super-Resolution: Orientation Operator Transformer—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
Towards Flexible, Scalable, and Adaptive Multi-Modal Conditioned Face Synthesis—0
A Comprehensive Study of Object Tracking in Low-Light Environments—0
High-Fidelity Diffusion-based Image Editing—0
Diffusion-EXR: Controllable Review Generation for Explainable Recommendation via Diffusion Models—0
Automatic Tooth Arrangement with Joint Features of Point and Mesh Representations via Diffusion Probabilistic Models—0
AI Generated Signal for Wireless Sensing—0
Emage: Non-Autoregressive Text-to-Image Generation—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