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

Showing 19261950 of 7282 papers

TitleStatusHype
Denoise-I2W: Mapping Images to Denoising Words for Accurate Zero-Shot Composed Image RetrievalCode0
LGC-Net: A Lightweight Gyroscope Calibration Network for Efficient Attitude EstimationCode0
Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life PredictionCode0
Leveraging Deep Stein's Unbiased Risk Estimator for Unsupervised X-ray DenoisingCode0
DenoMAE: A Multimodal Autoencoder for Denoising Modulation SignalsCode0
Leveraging Self-supervised Denoising for Image SegmentationCode0
Lifting Layers: Analysis and ApplicationsCode0
Dendritic error backpropagation in deep cortical microcircuitsCode0
LED: A Large-scale Real-world Paired Dataset for Event Camera DenoisingCode0
BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy MatchingCode0
Démélange, déconvolution et débruitage conjoints d'un modèle convolutif parcimonieux avec dérive instrumentale, par pénalisation de rapports de normes ou quasi-normes lissées (PENDANTSS)Code0
Benchmarking multi-component signal processing methods in the time-frequency planeCode0
Adaptive Long-term Embedding with Denoising and Augmentation for RecommendationCode0
Learning with Noisy Labels by Adaptive Gradient-Based Outlier RemovalCode0
Learning to Reach Goals via DiffusionCode0
BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented DialogCode0
Noise Adaption Network for Morse Code Image ClassificationCode0
Learning to Kindle the StarlightCode0
Learning to Generate Samples from Noise through Infusion TrainingCode0
Learning to Separate Object Sounds by Watching Unlabeled VideoCode0
Let SSMs be ConvNets: State-space Modeling with Optimal Tensor ContractionsCode0
MambaFoley: Foley Sound Generation using Selective State-Space ModelsCode0
Learning to compress and search visual data in large-scale systemsCode0
Learning to Bound: A Generative Cramér-Rao BoundCode0
Learning to Decouple and Generate Seismic Random Noise via Invertible Neural NetworkCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified