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 6651–6700 of 7282 papers

TitleStatusHype
Learning to Bound: A Generative Cramér-Rao BoundCode0
A Theoretically Guaranteed Quaternion Weighted Schatten p-norm Minimization Method for Color Image RestorationCode0
Semantic Adversarial ExamplesCode0
Learning to compress and search visual data in large-scale systemsCode0
Learning to Decouple and Generate Seismic Random Noise via Invertible Neural NetworkCode0
Learning to Denoise Distantly-Labeled Data for Entity TypingCode0
New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-ResolutionCode0
Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction PredictionCode0
Unsupervised Hyperspectral Stimulated Raman Microscopy Image Enhancement: Denoising and Segmentation via One-Shot Deep LearningCode0
Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-RegulationCode0
Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance NormalizationCode0
DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNsCode0
Graph Signal Recovery Using Restricted Boltzmann MachinesCode0
Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample FilteringCode0
Topological Slepians: Maximally Localized Representations of Signals over Simplicial ComplexesCode0
CoSTI: Consistency Models for (a faster) Spatio-Temporal ImputationCode0
ACT-Diffusion: Efficient Adversarial Consistency Training for One-step Diffusion ModelsCode0
Graph topology inference benchmarks for machine learningCode0
NLH: A Blind Pixel-level Non-local Method for Real-world Image DenoisingCode0
Learning to Generate Samples from Noise through Infusion TrainingCode0
Learning to Kindle the StarlightCode0
Semantic Correlation Promoted Shape-Variant Context for SegmentationCode0
xUnit: Learning a Spatial Activation Function for Efficient Image RestorationCode0
DiffusionTrack: Point Set Diffusion Model for Visual Object TrackingCode0
Grids Often Outperform Implicit Neural RepresentationsCode0
NMformer: A Transformer for Noisy Modulation Classification in Wireless CommunicationCode0
Algorithmic Guarantees for Inverse Imaging with Untrained Network PriorsCode0
Ground Truth Free Denoising by Optimal TransportCode0
Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational PosteriorsCode0
Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising AutoencodersCode0
Textual and Visual Prompt Fusion for Image Editing via Step-Wise AlignmentCode0
Learning to Reach Goals via DiffusionCode0
Total Denoising: Unsupervised Learning of 3D Point Cloud CleaningCode0
Consistent Human Image and Video Generation with Spatially Conditioned DiffusionCode0
CoStoDet-DDPM: Collaborative Training of Stochastic and Deterministic Models Improves Surgical Workflow Anticipation and RecognitionCode0
Learning to Separate Object Sounds by Watching Unlabeled VideoCode0
NODE-ImgNet: a PDE-informed effective and robust model for image denoisingCode0
VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video DenoisingCode0
Posterior Sampling for Random Noise Attenuation via Score-based Generative ModelsCode0
On the Wasserstein Convergence and Straightness of Rectified FlowCode0
Simultaneous Fidelity and Regularization Learning for Image RestorationCode0
Learning with Noisy Labels by Adaptive Gradient-Based Outlier RemovalCode0
Revisit 1D Total Variation restoration problem with new real-time algorithms for signal and hyper-parameter estimationsCode0
Straight to Shapes: Real-time Detection of Encoded ShapesCode0
Noise2Info: Noisy Image to Information of Noise for Self-Supervised Image DenoisingCode0
Guided and Variance-Corrected Fusion with One-shot Style Alignment for Large-Content Image GenerationCode0
Posterior temperature optimized Bayesian models for inverse problems in medical imagingCode0
LED: A Large-scale Real-world Paired Dataset for Event Camera DenoisingCode0
SemiDDM-Weather: A Semi-supervised Learning Framework for All-in-one Adverse Weather RemovalCode0
CocoNet: A deep neural network for mapping pixel coordinates to color valuesCode0
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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