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 3951–3975 of 7282 papers

TitleStatusHype
GDTS: Goal-Guided Diffusion Model with Tree Sampling for Multi-Modal Pedestrian Trajectory Prediction—0
GCRayDiffusion: Pose-Free Surface Reconstruction via Geometric Consistent Ray Diffusion—0
GECCO: Geometrically-Conditioned Point Diffusion Models—0
GEM3D: GEnerative Medial Abstractions for 3D Shape Synthesis—0
GenCAD-Self-Repairing: Feasibility Enhancement for 3D CAD Generation—0
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning—0
General Intelligent Imaging and Uncertainty Quantification by Deterministic Diffusion Model—0
Generalization error bound for denoising score matching under relaxed manifold assumption—0
Double Descent and Overfitting under Noisy Inputs and Distribution Shift for Linear Denoisers—0
VITON-DiT: Learning In-the-Wild Video Try-On from Human Dance Videos via Diffusion Transformers—0
ViTs are Everywhere: A Comprehensive Study Showcasing Vision Transformers in Different Domain—0
3DDesigner: Towards Photorealistic 3D Object Generation and Editing with Text-guided Diffusion Models—0
Generalized Intersection Algorithms with Fixpoints for Image Decomposition Learning—0
Generalized linear models with low rank effects for network data—0
ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer—0
From Group Sparse Coding to Rank Minimization: A Novel Denoising Model for Low-level Image Restoration—0
Adversary-Robust Graph-Based Learning of WSIs—0
Generalized Tensor Total Variation Minimization for Visual Data Recovery—0
Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling—0
HRFA: High-Resolution Feature-based Attack—0
Generate Natural Language Explanations for Recommendation—0
Generating Bug-Fixes Using Pretrained Transformers—0
Generating, Fast and Slow: Scalable Parallel Video Generation with Video Interface Networks—0
Generating Fluent Translations from Disfluent Text Without Access to Fluent References: IIT Bombay@IWSLT2020—0
Generating Full-field Evolution of Physical Dynamics from Irregular Sparse Observations—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