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 3651–3700 of 7282 papers

TitleStatusHype
Leveraging Denoised Abstract Meaning Representation for Grammatical Error Correction—0
Leveraging Diffusion Models for Parameterized Quantum Circuit Generation—0
Leveraging Diffusion Models for Synthetic Data Augmentation in Protein Subcellular Localization Classification—0
Leveraging Fine-Grained Information and Noise Decoupling for Remote Sensing Change Detection—0
Leveraging Graph Diffusion Models for Network Refinement Tasks—0
Leveraging Side Information for Ligand Conformation Generation using Diffusion-Based Approaches—0
Leveraging Text Repetitions and Denoising Autoencoders in OCR Post-correction—0
Leveraging the Spatial Hierarchy: Coarse-to-fine Trajectory Generation via Cascaded Hybrid Diffusion—0
TouchTTS: An Embarrassingly Simple TTS Framework that Everyone Can Touch—0
Zero-shot Generation of Training Data with Denoising Diffusion Probabilistic Model for Handwritten Chinese Character Recognition—0
Toward Accurate Camera-based 3D Object Detection via Cascade Depth Estimation and Calibration—0
LGDN: Language-Guided Denoising Network for Video-Language Modeling—0
LiDAR-Aided Mobile Blockage Prediction in Real-World Millimeter Wave Systems—0
LiDAR Ground Filtering Algorithm for Urban Areas Using Scan Line Based Segmentation—0
Lifted Bregman Training of Neural Networks—0
Wave-shape Function Model Order Estimation by Trigonometric Regression—0
Light Field Denoising via Anisotropic Parallax Analysis in a CNN Framework—0
Light Field Diffusion for Single-View Novel View Synthesis—0
A Deep Learning Approach to Data-driven Parameterizations for Statistical Parametric Speech Synthesis—0
Lightweight Language-driven Grasp Detection using Conditional Consistency Model—0
Lightweight Physics-Informed Zero-Shot Ultrasound Plane Wave Denoising—0
Lightweight Video Denoising Using a Classic Bayesian Backbone—0
Likelihood-based Out-of-Distribution Detection with Denoising Diffusion Probabilistic Models—0
LIME: Localized Image Editing via Attention Regularization in Diffusion Models—0
Limited-Angle CBCT Reconstruction via Geometry-Integrated Cycle-domain Denoising Diffusion Probabilistic Models—0
Limited Angle Tomography for Transmission X-Ray Microscopy Using Deep Learning—0
Limited Parameter Denoising for Low-dose X-ray Computed Tomography Using Deep Reinforcement Learning—0
Limit Order Book Event Stream Prediction with Diffusion Model—0
Linear Attention Based Deep Nonlocal Means Filtering for Multiplicative Noise Removal—0
Nearly d-Linear Convergence Bounds for Diffusion Models via Stochastic Localization—0
Linearized ADMM and Fast Nonlocal Denoising for Efficient Plug-and-Play Restoration—0
A Deep Generative Model for the Simulation of Discrete Karst Networks—0
A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction—0
WaveTransfer: A Flexible End-to-end Multi-instrument Timbre Transfer with Diffusion—0
Toward Efficient Deep Blind RAW Image Restoration—0
Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network—0
Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes—0
Toward Moiré-Free and Detail-Preserving Demosaicking—0
Adapting Language Models for Non-Parallel Author-Stylized Rewriting—0
List-Mode PET Image Reconstruction Using Dykstra-Like Splitting—0
LITA-GS: Illumination-Agnostic Novel View Synthesis via Reference-Free 3D Gaussian Splatting and Physical Priors—0
Towards Adversarial Denoising of Radar Micro-Doppler Signatures—0
Towards Adversarially Robust Deep Image Denoising—0
Live2Diff: Live Stream Translation via Uni-directional Attention in Video Diffusion Models—0
Towards Adversarial Purification using Denoising AutoEncoders—0
LLM4DSR: Leveraing Large Language Model for Denoising Sequential Recommendation—0
LLMs Help Alleviate the Cross-Subject Variability in Brain Signal and Language Alignment—0
LLVD: LSTM-based Explicit Motion Modeling in Latent Space for Blind Video Denoising—0
Local Activity-tuned Image Filtering for Noise Removal and Image Smoothing—0
Local Averaging Accurately Distills Manifold Structure From Noisy Data—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