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

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Showing 351–400 of 7282 papers

TitleStatusHype
CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors—0
Fast Autoregressive Models for Continuous Latent Generation—0
Self-Supervised Noise Adaptive MRI Denoising via Repetition to Repetition (Rep2Rep) Learning—0
Evolution Meets Diffusion: Efficient Neural Architecture Generation—0
A Machine Learning Approach for Denoising and Upsampling HRTFs—0
ECGDeDRDNet: A deep learning-based method for Electrocardiogram noise removal using a double recurrent dense network—0
Diffusion Probabilistic Models for Compressive SAR Imaging—0
Physics-guided and fabrication-aware inverse design of photonic devices using diffusion modelsCode1
Survey of Video Diffusion Models: Foundations, Implementations, and ApplicationsCode1
Self-Controlled Diffusion for Denoising in Scientific Imaging—0
Aerial Active STAR-RIS-assisted Satellite-Terrestrial Covert Communications—0
An Automated Pipeline for Few-Shot Bird Call Classification: A Case Study with the Tooth-Billed PigeonCode0
Learned Primal Dual Splitting for Self-Supervised Noise-Adaptive MRI Reconstruction—0
A Self-supervised Learning Method for Raman Spectroscopy based on Masked Autoencoders—0
DyST-XL: Dynamic Layout Planning and Content Control for Compositional Text-to-Video Generation—0
PIV-FlowDiffuser:Transfer-learning-based denoising diffusion models for PIVCode0
FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models—0
An Artificial Intelligence Enabled Signature Estimation of Dual Wideband Systems in Ultra-Low Signal-to-Noise Ratio—0
Text-Audio-Visual-conditioned Diffusion Model for Video Saliency Prediction—0
Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided CalibrationCode1
Generative emulation of chaotic dynamics with coherent prior—0
OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone InscriptionsCode0
WeatherGen: A Unified Diverse Weather Generator for LiDAR Point Clouds via Spider Mamba DiffusionCode1
Filter2Noise: Interpretable Self-Supervised Single-Image Denoising for Low-Dose CT with Attention-Guided Bilateral FilteringCode1
Adaptive Long-term Embedding with Denoising and Augmentation for RecommendationCode0
Towards a Multi-Agent Vision-Language System for Zero-Shot Novel Hazardous Object Detection for Autonomous Driving SafetyCode0
Denoising and Reconstruction of Nonlinear Dynamics using Truncated Reservoir Computing—0
TTRD3: Texture Transfer Residual Denoising Dual Diffusion Model for Remote Sensing Image Super-ResolutionCode1
Set You Straight: Auto-Steering Denoising Trajectories to Sidestep Unwanted ConceptsCode3
The Tenth NTIRE 2025 Image Denoising Challenge ReportCode1
Towards Explainable Fusion and Balanced Learning in Multimodal Sentiment Analysis—0
Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging—0
Generative Recommendation with Continuous-Token Diffusion—0
Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders—0
Generalization through variance: how noise shapes inductive biases in diffusion modelsCode0
Exploring Video-Based Driver Activity Recognition under Noisy LabelsCode0
Bringing together invertible UNets with invertible attention modules for memory-efficient diffusion models—0
IlluSign: Illustrating Sign Language Videos by Leveraging the Attention Mechanism—0
Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual PerceptionCode1
Cross-Frequency Implicit Neural Representation with Self-Evolving Parameters—0
ADT: Tuning Diffusion Models with Adversarial Supervision—0
Taming Consistency Distillation for Accelerated Human Image Animation—0
Efficient and Robust Remote Sensing Image Denoising Using Randomized Approximation of Geodesics' Gramian on the Manifold Underlying the Patch Space—0
AgentPolyp: Accurate Polyp Segmentation via Image Enhancement Agent—0
Efficient Medical Image Restoration via Reliability Guided Learning in Frequency Domain—0
Predicting Wave Dynamics using Deep Learning with Multistep Integration Inspired Attention and Physics-Based Loss Decomposition—0
InterAnimate: Taming Region-aware Diffusion Model for Realistic Human Interaction Animation—0
Ising Models with Hidden Markov Structure: Applications to Probabilistic Inference in Machine Learning—0
Invariance Matters: Empowering Social Recommendation via Graph Invariant LearningCode1
DiffMOD: Progressive Diffusion Point Denoising for Moving Object Detection in Remote Sensing—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