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 5501–5550 of 7282 papers

TitleStatusHype
Representation, Analysis of Bayesian Refinement Approximation Network: A Survey—0
Training a Task-Specific Image Reconstruction Loss—0
Weakly-supervised Audio-visual Sound Source Detection and Separation—0
JDSR-GAN: Constructing An Efficient Joint Learning Network for Masked Face Super-Resolution—0
Task-Oriented Low-Dose CT Image DenoisingCode0
Deep Learning with robustness to missing data: A novel approach to the detection of COVID-19—0
Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition—0
MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising—0
Finite Impulse Response Filters for Simplicial Complexes—0
TSTNN: Two-stage Transformer based Neural Network for Speech Enhancement in the Time Domain—0
New Computational Techniques for a Faster Variation of BM3D Image Denoising—0
Data Discovery Using Lossless Compression-Based Sparse Representation—0
Fast and Accurate: Video Enhancement using Sparse Depth—0
Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparisonCode0
Exact Sparse Orthogonal Dictionary Learning—0
Hyperspectral Image Denoising and Anomaly Detection Based on Low-rank and Sparse RepresentationsCode0
iToF2dToF: A Robust and Flexible Representation for Data-Driven Time-of-Flight Imaging—0
A Neural Network for SemigroupsCode0
An unsupervised deep learning framework for medical image denoising—0
Learning Feature Weights using Reward Modeling for Denoising Parallel Corpora—0
Robust graph-filter identification with graph denoising regularizationCode0
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie—0
Convolutional Neural Network Denoising in Fluorescence Lifetime Imaging Microscopy (FLIM)Code0
HTMD-Net: A Hybrid Masking-Denoising Approach to Time-Domain Monaural Singing Voice Separation—0
Prediction of financial time series using LSTM and data denoising methods—0
BM3D vs 2-Layer ONN—0
Convolutional versus Self-Organized Operational Neural Networks for Real-World Blind Image DenoisingCode0
DeepFN: Towards Generalizable Facial Action Unit Recognition with Deep Face Normalization—0
Feature-Align Network with Knowledge Distillation for Efficient Denoising—0
Solving Inverse Problems by Joint Posterior Maximization with Autoencoding PriorCode0
Efficient Deep Image Denoising via Class Specific Convolution—0
Deep Learning strategies for ProtoDUNE raw data denoisingCode0
Image denoising using complex-valued deep CNNCode0
Unsupervised dynamic modeling of medical image transformationCode0
Should EBMs model the energy or the score?—0
Deep learning based electrical noise removal enables high spectral optoacoustic contrast in deep tissue—0
Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting—0
Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priors—0
Handling Background Noise in Neural Speech Generation—0
EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks—0
Spatial-temporal switching estimators for imaging locally concentrated dynamics—0
Interpretable Stability Bounds for Spectral Graph Filters—0
NFCNN: Toward a Noise Fusion Convolutional Neural Network for Image Denoising—0
Training Stacked Denoising Autoencoders for Representation Learning—0
Orthogonal Features-based EEG Signal Denoising using Fractionally Compressed AutoEncoder—0
Context-Aware Prosody Correction for Text-Based Speech Editing—0
A Sub-band Approach to Deep Denoising Wavelet Networks and a Frequency-adaptive Loss for Perceptual Quality—0
Learning from Natural Noise to Denoise Micro-Doppler Spectrogram—0
Improving Zero-shot Neural Machine Translation on Language-specific Encoders-Decoders—0
Multimodal Data Visualization and Denoising with Integrated Diffusion—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