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 226–250 of 7282 papers

TitleStatusHype
Decoding Phone Pairs from MEG Signals Across Speech ModalitiesCode0
DeepKD: A Deeply Decoupled and Denoised Knowledge Distillation TrainerCode1
Adaptive Estimation and Learning under Temporal Distribution Shift—0
Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering—0
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off—0
Non-rigid Motion Correction for MRI Reconstruction via Coarse-To-Fine Diffusion Models—0
Time Series Similarity Score Functions to Monitor and Interact with the Training and Denoising Process of a Time Series Diffusion Model applied to a Human Activity Recognition Dataset based on IMUs—0
Multi-Channel Swin Transformer Framework for Bearing Remaining Useful Life Prediction—0
Communication-Efficient Diffusion Denoising Parallelization via Reuse-then-Predict Mechanism—0
RefiDiff: Refinement-Aware Diffusion for Efficient Missing Data Imputation—0
Adaptive Cyclic Diffusion for Inference Scaling—0
Neural Inverse Scattering with Score-based Regularization—0
AquaSignal: An Integrated Framework for Robust Underwater Acoustic Analysis—0
Improving Noise Robustness of LLM-based Zero-shot TTS via Discrete Acoustic Token Denoising—0
Stochastic Orthogonal Regularization for deep projective priors—0
Higher fidelity perceptual image and video compression with a latent conditioned residual denoising diffusion modelCode0
AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool UseCode1
Restoration Score Distillation: From Corrupted Diffusion Pretraining to One-Step High-Quality Generation—0
Degradation-Aware Feature Perturbation for All-in-One Image RestorationCode2
FlowPure: Continuous Normalizing Flows for Adversarial PurificationCode1
RoPECraft: Training-Free Motion Transfer with Trajectory-Guided RoPE Optimization on Diffusion Transformers—0
Anti-Inpainting: A Proactive Defense against Malicious Diffusion-based Inpainters under Unknown Conditions—0
Denoising Diffusion Probabilistic Model for Point Cloud Compression at Low Bit-RatesCode0
Addressing Missing Data Issue for Diffusion-based RecommendationCode0
CTLformer: A Hybrid Denoising Model Combining Convolutional Layers and Self-Attention for Enhanced CT Image Reconstruction—0
Show:102550
← PrevPage 10 of 292Next →
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