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 5401–5450 of 7282 papers

TitleStatusHype
A New Data Representation Based on Training Data Characteristics to Extract Drug Named-Entity in Medical Text—0
Attention-Driven Training-Free Efficiency Enhancement of Diffusion Models—0
Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models!—0
A Tunable Despeckling Neural Network Stabilized via Diffusion Equation—0
Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering—0
A Two-stage Complex Network using Cycle-consistent Generative Adversarial Networks for Speech Enhancement—0
A two-stage learning method for protein-protein interaction prediction—0
A Two-stage Method for Non-extreme Value Salt-and-Pepper Noise Removal—0
Simultaneous Inpainting and Denoising by Directional Global Three-part Decomposition: Connecting Variational and Fourier Domain Based Image Processing—0
Audio Attacks and Defenses against AED Systems -- A Practical Study—0
Audio Classical Composer Identification by Deep Neural Network—0
Audio Denoising for Robust Audio Fingerprinting—0
AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models—0
Augmented Robust PCA For Foreground-Background Separation on Noisy, Moving Camera Video—0
A new ADMM algorithm for the Euclidean median and its application to robust patch regression—0
A Unified Approach to Analysis and Design of Denoising Markov Models—0
A Unified Conditional Framework for Diffusion-based Image Restoration—0
A Unified Framework for Multi-Sensor HDR Video Reconstruction—0
A unified framework of non-local parametric methods for image denoising—0
A Unified Plug-and-Play Framework for Effective Data Denoising and Robust Abstention—0
A Unified View Between Tensor Hypergraph Neural Networks And Signal Denoising—0
A Universal Deep Learning Framework for Real-Time Denoising of Ultrasound Images—0
AuthentiGPT: Detecting Machine-Generated Text via Black-Box Language Models Denoising—0
AutoDenoise: Automatic Data Instance Denoising for Recommendations—0
Autoencoders as Weight Initialization of Deep Classification Networks for Cancer versus Cancer Studies—0
Auto-encoders: reconstruction versus compression—0
Autoencoding Random Forests—0
Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network—0
Automated Channel Pruning with Learned Importance—0
Automated Data Denoising for Recommendation—0
Automated Learning of Semantic Embedding Representations for Diffusion Models—0
Automated OCT Segmentation for Images with DME—0
Simultaneous reconstruction and displacement estimation for spectral-domain optical coherence elastography—0
Automatic artifact removal of resting-state fMRI with Deep Neural Networks—0
Automatic Detection of ECG Abnormalities by using an Ensemble of Deep Residual Networks with Attention—0
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence—0
Simultaneous Sparse Dictionary Learning and Pruning—0
Automatic Foreground Extraction from Imperfect Backgrounds using Multi-Agent Consensus Equilibrium—0
Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images—0
Automatic Image Annotation via Label Transfer in the Semantic Space—0
Automatic Lumbar Spinal CT Image Segmentation with a Dual Densely Connected U-Net—0
Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-heuristically Optimized Non-local Means Filter—0
Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content—0
Automatic Summarization for Creative Writing: BART based Pipeline Method for Generating Summary of Movie Scripts—0
Automatic Tooth Arrangement with Joint Features of Point and Mesh Representations via Diffusion Probabilistic Models—0
Autonomous Deep Learning: Incremental Learning of Denoising Autoencoder for Evolving Data Streams—0
Autonomous Point Cloud Segmentation for Power Lines Inspection in Smart Grid—0
Simultaneous Tensor Completion and Denoising by Noise Inequality Constrained Convex Optimization—0
Autoregressive Moving Average Graph Filtering—0
Autoregressive Score Matching—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