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 1901–1950 of 7282 papers

TitleStatusHype
Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging ProblemsCode0
Magicremover: Tuning-free Text-guided Image inpainting with Diffusion ModelsCode0
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy TrainingCode0
Machine learning based lens-free imaging technique for field-portable cytometryCode0
Denoising-based Contractive Imitation LearningCode0
Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial DefenseCode0
Denoising autoencoder with modulated lateral connections learns invariant representations of natural imagesCode0
Low Frequency Adversarial PerturbationCode0
LIDIA: Lightweight Learned Image Denoising with Instance AdaptationCode0
Low-Complexity Blind Parameter Estimation in Wireless Systems with Noisy Sparse SignalsCode0
LoFi: Neural Local Fields for Scalable Image ReconstructionCode0
Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse CodingCode0
Denoising Architecture for Unsupervised Anomaly Detection in Time-SeriesCode0
Boosted Locality Sensitive Hashing: Discriminative Binary Codes for Source SeparationCode0
Beyond Human Perception: Understanding Multi-Object World from Monocular ViewCode0
Localized Fourier Analysis for Graph Signal ProcessingCode0
Denoising and Regularization via Exploiting the Structural Bias of Convolutional GeneratorsCode0
Negligible effect of brain MRI data preprocessing for tumor segmentationCode0
LLNet: A Deep Autoencoder Approach to Natural Low-light Image EnhancementCode0
LMD: Faster Image Reconstruction with Latent Masking DiffusionCode0
Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold SimplificationCode0
LINN: Lifting Inspired Invertible Neural Network for Image DenoisingCode0
Linking Sketch Patches by Learning Synonymous Proximity for Graphic Sketch RepresentationCode0
Linear-Time Algorithm in Bayesian Image Denoising based on Gaussian Markov Random FieldCode0
LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation LearningCode0
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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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