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 1876–1900 of 7282 papers

TitleStatusHype
Hume: Introducing System-2 Thinking in Visual-Language-Action Model—0
Be Decisive: Noise-Induced Layouts for Multi-Subject Generation—0
Autoencoding Random Forests—0
A Graph Completion Method that Jointly Predicts Geometry and Topology Enables Effective Molecule Assembly—0
NatADiff: Adversarial Boundary Guidance for Natural Adversarial Diffusion—0
InstGenIE: Generative Image Editing Made Efficient with Mask-aware Caching and Scheduling—0
Leveraging Diffusion Models for Parameterized Quantum Circuit Generation—0
Conditional Diffusion Models with Classifier-Free Gibbs-like GuidanceCode0
STITCH-OPE: Trajectory Stitching with Guided Diffusion for Off-Policy Evaluation—0
Refining Few-Step Text-to-Multiview Diffusion via Reinforcement LearningCode0
Deep Spectral Prior—0
MotionPro: A Precise Motion Controller for Image-to-Video Generation—0
Structure Disruption: Subverting Malicious Diffusion-Based Inpainting via Self-Attention Query Perturbation—0
ICDM: Interference Cancellation Diffusion Models for Wireless Semantic Communications—0
Ankh3: Multi-Task Pretraining with Sequence Denoising and Completion Enhances Protein Representations—0
UltraVSR: Achieving Ultra-Realistic Video Super-Resolution with Efficient One-Step Diffusion Space—0
Exploring Magnitude Preservation and Rotation Modulation in Diffusion Transformers—0
Enhancing Text-to-Image Diffusion Transformer via Split-Text Conditioning—0
VL-SAM-V2: Open-World Object Detection with General and Specific Query Fusion—0
Test-Time Scaling of Diffusion Models via Noise Trajectory SearchCode0
SW-ViT: A Spatio-Temporal Vision Transformer Network with Post Denoiser for Sequential Multi-Push Ultrasound Shear Wave Elastography—0
On Denoising Walking Videos for Gait Recognition—0
Restoring Real-World Images with an Internal Detail Enhancement Diffusion Model—0
Image denoising as a conditional expectation—0
Brightness-Invariant Tracking Estimation in Tagged MRI—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