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 7126–7150 of 7282 papers

TitleStatusHype
Deep Learning-Based Channel EstimationCode0
Evaluating Unsupervised Denoising Requires Unsupervised MetricsCode0
Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled DataCode0
An Underparametrized Deep Decoder Architecture for Graph SignalsCode0
Improving Hypernymy Extraction with Distributional Semantic ClassesCode0
Complex Image Generation SwinTransformer Network for Audio DenoisingCode0
The Intel Neuromorphic DNS ChallengeCode0
Event-based Camera Simulation using Monte Carlo Path Tracing with Adaptive DenoisingCode0
Lightweight network towards real-time image denoising on mobile devicesCode0
MFM-DA: Instance-Aware Adaptor and Hierarchical Alignment for Efficient Domain Adaptation in Medical Foundation ModelsCode0
Event-driven Video Frame SynthesisCode0
Deep learning-based deconvolution for interferometric radio transient reconstructionCode0
On the Noise Sensitivity of the Randomized SVDCode0
MFTF: Mask-free Training-free Object Level Layout Control Diffusion ModelCode0
Can We Transfer Noise Patterns? A Multi-environment Spectrum Analysis Model Using Generated CasesCode0
Sampling with flows, diffusion and autoregressive neural networks: A spin-glass perspectiveCode0
Balancing the Style-Content Trade-Off in Sentiment Transfer Using Polarity-Aware DenoisingCode0
Zero-Shot Denoising for Fluorescence Lifetime Imaging Microscopy with Intensity-Guided LearningCode0
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMMCode0
On the Perturbed States for Transformed Input-robust Reinforcement LearningCode0
RH-Net: Improving Neural Relation Extraction via Reinforcement Learning and Hierarchical Relational SearchingCode0
Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising ApproachCode0
Improving Robustness to Model Inversion Attacks via Sparse Coding ArchitecturesCode0
ExactDreamer: High-Fidelity Text-to-3D Content Creation via Exact Score MatchingCode0
Microscopy Image Restoration with Deep Wiener-Kolmogorov filtersCode0
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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