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 2226–2250 of 7282 papers

TitleStatusHype
Deep Perceptual Enhancement for Medical Image AnalysisCode0
GPT-PPG: A GPT-based Foundation Model for Photoplethysmography Signals—0
Reconstruct Anything Model: a lightweight foundation model for computational imaging—0
Inversion-Free Video Style Transfer with Trajectory Reset Attention Control and Content-Style Bridging—0
Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation—0
Denoising Hamiltonian Network for Physical Reasoning—0
LatexBlend: Scaling Multi-concept Customized Generation with Latent Textual Blending—0
Post-Training Quantization for Diffusion Transformer via Hierarchical Timestep Grouping—0
Graph Chirp Signal and Graph Fractional Vertex-Frequency Energy Distribution—0
Two-stage Deep Denoising with Self-guided Noise Attention for Multimodal Medical Images—0
Exposure Bias Reduction for Enhancing Diffusion Transformer Feature CachingCode0
Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising—0
Generative method for aerodynamic optimization based on classifier-free guided denoising diffusion probabilistic model—0
MIGA: Mutual Information-Guided Attack on Denoising Models for Semantic Manipulation—0
TIDE : Temporal-Aware Sparse Autoencoders for Interpretable Diffusion Transformers in Image Generation—0
PixelPonder: Dynamic Patch Adaptation for Enhanced Multi-Conditional Text-to-Image Generation—0
Federated Learning for Diffusion Models—0
Speech Audio Generation from dynamic MRI via a Knowledge Enhanced Conditional Variational Autoencoder—0
ProSE: Diffusion Priors for Speech Enhancement—0
D3DR: Lighting-Aware Object Insertion in Gaussian Splatting—0
Diffusion Model Based Probabilistic Day-ahead Load Forecasting—0
PointDiffuse: A Dual-Conditional Diffusion Model for Enhanced Point Cloud Semantic Segmentation—0
Explainable Synthetic Image Detection through Diffusion Timestep Ensembling—0
WaveStitch: Flexible and Fast Conditional Time Series Generation with Diffusion ModelsCode0
MagicInfinite: Generating Infinite Talking Videos with Your Words and Voice—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