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 2826–2850 of 7282 papers

TitleStatusHype
MedDiff-FM: A Diffusion-based Foundation Model for Versatile Medical Image Applications—0
IKDP: Inverse Kinematics through Diffusion Process—0
DEL-Ranking: Ranking-Correction Denoising Framework for Elucidating Molecular Affinities in DNA-Encoded Libraries—0
Retrieval Augmented Diffusion Model for Structure-informed Antibody Design and Optimization—0
On the Wasserstein Convergence and Straightness of Rectified FlowCode0
Unified Convergence Analysis for Score-Based Diffusion Models with Deterministic Samplers—0
RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training—0
Multi-modal Pose Diffuser: A Multimodal Generative Conditional Pose Prior—0
Heavy-Tailed Diffusion Models—0
SGD Jittering: A Training Strategy for Robust and Accurate Model-Based Architectures—0
Dynamic Negative Guidance of Diffusion ModelsCode0
Truncated Consistency Models—0
DiffImp: Efficient Diffusion Model for Probabilistic Time Series Imputation with Bidirectional Mamba Backbone—0
Theory on Score-Mismatched Diffusion Models and Zero-Shot Conditional Samplers—0
Latent Weight Diffusion: Generating reactive policies instead of trajectories—0
Islanding Detection for Active Distribution Networks Using WaveNet+UNet Classifier—0
FDF: Flexible Decoupled Framework for Time Series Forecasting with Conditional Denoising and Polynomial ModelingCode0
Meta-DiffuB: A Contextualized Sequence-to-Sequence Text Diffusion Model with Meta-ExplorationCode0
Enhancing Crowdsourced Audio for Text-to-Speech Models—0
Label-free prediction of fluorescence markers in bovine satellite cells using deep learning—0
The Latent Road to Atoms: Backmapping Coarse-grained Protein Structures with Latent Diffusion—0
Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation—0
MING: A Functional Approach to Learning Molecular Generative Models—0
Beyond Oversmoothing: Evaluating DDPM and MSE for Scalable Speech Synthesis in ASR—0
Evaluating Utility of Memory Efficient Medical Image Generation: A Study on Lung Nodule Segmentation—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