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 2901–2950 of 7282 papers

TitleStatusHype
A First Derivative Potts Model for Segmentation and Denoising Using ILP—0
Explainable Deep Learning Framework for SERS Bio-quantification—0
Explainable Artificial Intelligence driven mask design for self-supervised seismic denoising—0
FEMSN: Frequency-Enhanced Multiscale Network for fault diagnosis of rotating machinery under strong noise environments—0
Coop-WD: Cooperative Perception with Weighting and Denoising for Robust V2V Communication—0
Fetal Gender Identification using Machine and Deep Learning Algorithms on Phonocardiogram Signals—0
Example-based super-resolution for point-cloud video—0
Few-Shot Concept Unlearning with Low Rank Adaptation—0
Examining the Mapping Functions of Denoising Autoencoders in Singing Voice Separation—0
Cooperative Denoising for Distantly Supervised Relation Extraction—0
A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration—0
Few-Shot Meta-Denoising—0
D²Net: A Denoising and Dereverberation Network Based on Two-branch Encoder and Dual-path Transformer—0
FFDNet-Based Channel Estimation for Massive MIMO Visible Light Communication Systems—0
Examining Joint Demosaicing and Denoising for Single-, Quad-, and Nona-Bayer Patterns—0
Exact Sparse Orthogonal Dictionary Learning—0
Cooling-Guide Diffusion Model for Battery Cell Arrangement—0
EWT: Efficient Wavelet-Transformer for Single Image Denoising—0
Convolution Type of Metaplectic Cohen's Distribution Time-Frequency Analysis Theory, Method and Technology—0
Reversing Skin Cancer Adversarial Examples by Multiscale Diffusive and Denoising Aggregation Mechanism—0
6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimation—0
Diffusion Transformer-based Universal Dose Denoising for Pencil Beam Scanning Proton Therapy—0
Generative thermodynamic computing—0
Filter Forests for Learning Data-Dependent Convolutional Kernels—0
EVRNet: Efficient Video Restoration on Edge Devices—0
Financial Market Directional Forecasting With Stacked Denoising Autoencoder—0
Evolving Deep Convolutional Neural Networks for Hyperspectral Image Denoising—0
Convolution by Evolution: Differentiable Pattern Producing Networks—0
Evolvable Conditional Diffusion—0
Evolution Meets Diffusion: Efficient Neural Architecture Generation—0
ASGDiffusion: Parallel High-Resolution Generation with Asynchronous Structure Guidance—0
A fast patch-dictionary method for whole image recovery—0
Evolutionary Variational Optimization of Generative Models—0
Fine-Tuning Hybrid Physics-Informed Neural Networks for Vehicle Dynamics Model Estimation—0
EventZoom: Learning To Denoise and Super Resolve Neuromorphic Events—0
Convolutional Sparse Coding with Overlapping Group Norms—0
Event-Guided Denoising for Multilingual Relation Learning—0
Fingerprinting Denoising Diffusion Probabilistic Models—0
A Tiered Move-making Algorithm for General Non-submodular Pairwise Energies—0
Finite Impulse Response Filters for Simplicial Complexes—0
FINO: Flow-based Joint Image and Noise Model—0
FireEdit: Fine-grained Instruction-based Image Editing via Region-aware Vision Language Model—0
A Set-Theoretic Study of the Relationships of Image Models and Priors for Restoration Problems—0
Event Guided Denoising for Multilingual Relation Learning—0
EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm—0
First order algorithms in variational image processing—0
Increasing Iterate Averaging for Solving Saddle-Point Problems—0
First Place Solution of 2023 Global Artificial Intelligence Technology Innovation Competition Track 1—0
Fitting very flexible models: Linear regression with large numbers of parameters—0
A sensitivity analysis to quantify the impact of neuroimaging preprocessing strategies on subsequent statistical analyses—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