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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1951–1975 of 7282 papers

TitleStatusHype
Low-Complexity Blind Parameter Estimation in Wireless Systems with Noisy Sparse SignalsCode0
Learning to Reach Goals via DiffusionCode0
Learning to Kindle the StarlightCode0
DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds DefenseCode0
Learning to Generate Samples from Noise through Infusion TrainingCode0
Learning to Separate Object Sounds by Watching Unlabeled VideoCode0
Despeckling Sentinel-1 GRD images by deep learning and application to narrow river segmentationCode0
BrainCodec: Neural fMRI codec for the decoding of cognitive brain statesCode0
DestripeCycleGAN: Stripe Simulation CycleGAN for Unsupervised Infrared Image DestripingCode0
Defending Observation Attacks in Deep Reinforcement Learning via Detection and DenoisingCode0
Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction PredictionCode0
Learning to Decouple and Generate Seismic Random Noise via Invertible Neural NetworkCode0
Defending Adversarial Attacks on Deep Learning Based Power Allocation in Massive MIMO Using Denoising AutoencodersCode0
Detect and Defense Against Adversarial Examples in Deep Learning using Natural Scene Statistics and Adaptive DenoisingCode0
Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision TransformersCode0
Detecting Patch Adversarial Attacks with Image ResidualsCode0
Learning to Denoise Distantly-Labeled Data for Entity TypingCode0
Learning to Bound: A Generative Cramér-Rao BoundCode0
Bayes-optimal learning of an extensive-width neural network from quadratically many samplesCode0
Learning to compress and search visual data in large-scale systemsCode0
Learning the Dynamic Correlations and Mitigating Noise by Hierarchical Convolution for Long-term Sequence ForecastingCode0
Understanding Galaxy Morphology Evolution Through Cosmic Time via Redshift Conditioned Diffusion ModelsCode0
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving AugmentationCode0
Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative RefinementCode0
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging ProblemsCode0
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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