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 4151–4175 of 7282 papers

TitleStatusHype
Synthesising Rare Cataract Surgery Samples with Guided Diffusion ModelsCode0
Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models—0
Adversarial Training of Denoising Diffusion Model Using Dual Discriminators for High-Fidelity Multi-Speaker TTS—0
DiffGANPaint: Fast Inpainting Using Denoising Diffusion GANs—0
Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment for Markup-to-Image GenerationCode0
Leveraging Side Information for Ligand Conformation Generation using Diffusion-Based Approaches—0
EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding—0
Can We Transfer Noise Patterns? A Multi-environment Spectrum Analysis Model Using Generated CasesCode0
Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Review—0
Decomposition Ascribed Synergistic Learning for Unified Image Restoration—0
PressureTransferNet: Human Attribute Guided Dynamic Ground Pressure Profile Transfer using 3D simulated Pressure Maps—0
Diffusion Model for Camouflaged Object DetectionCode0
Domain Adaptation based on Human Feedback for Enhancing Generative Model Denoising Abilities—0
Toward Zero-shot Character Recognition: A Gold Standard Dataset with Radical-level Annotations—0
Don't be so negative! Score-based Generative Modeling with Oracle-assisted Guidance—0
MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning—0
Contrastive Conditional Latent Diffusion for Audio-visual SegmentationCode0
DiffPose: SpatioTemporal Diffusion Model for Video-Based Human Pose Estimation—0
A Novel DDPM-based Ensemble Approach for Energy Theft Detection in Smart Grids—0
Fusing Sparsity with Deep Learning for Rotating Scatter Mask Gamma Imaging—0
RGB-D-Fusion: Image Conditioned Depth Diffusion of Humanoid SubjectsCode0
Exploring Format Consistency for Instruction TuningCode0
AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodies—0
TEDi: Temporally-Entangled Diffusion for Long-Term Motion Synthesis—0
Spatial-Frequency U-Net for Denoising Diffusion Probabilistic Models—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