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 2276–2300 of 7282 papers

TitleStatusHype
Informed Graph Learning By Domain Knowledge Injection and Smooth Graph Signal RepresentationCode0
CoStoDet-DDPM: Collaborative Training of Stochastic and Deterministic Models Improves Surgical Workflow Anticipation and RecognitionCode0
CoSTI: Consistency Models for (a faster) Spatio-Temporal ImputationCode0
Co-Separating Sounds of Visual ObjectsCode0
Inference Stage Denoising for Undersampled MRI ReconstructionCode0
Inference-Time Diffusion Model DistillationCode0
Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based PriorsCode0
Index NetworkCode0
IncSAR: A Dual Fusion Incremental Learning Framework for SAR Target RecognitionCode0
A Flag Decomposition for Hierarchical DatasetsCode0
Incomplete Gamma Kernels: Generalizing Locally Optimal Projection OperatorsCode0
A Showcase of the Use of Autoencoders in Feature Learning ApplicationsCode0
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal DenoisingCode0
Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-TuningCode0
Inexact Derivative-Free Optimization for Bilevel LearningCode0
Convolutional versus Self-Organized Operational Neural Networks for Real-World Blind Image DenoisingCode0
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMMCode0
Convolutional Neural Network with Median Layers for Denoising Salt-and-Pepper ContaminationsCode0
Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled DataCode0
Improving Hypernymy Extraction with Distributional Semantic ClassesCode0
A Semisupervised Approach for Language Identification based on Ladder NetworksCode0
RH-Net: Improving Neural Relation Extraction via Reinforcement Learning and Hierarchical Relational SearchingCode0
Convolutional Neural Networks Can Be Deceived by Visual IllusionsCode0
Improving Chinese Story Generation via Awareness of Syntactic Dependencies and SemanticsCode0
Accelerated Gradient Methods for Sparse Statistical Learning with Nonconvex PenaltiesCode0
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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