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 1851–1875 of 7282 papers

TitleStatusHype
MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-ResolutionCode0
Denoising Diffusion Probabilistic Model for Point Cloud Compression at Low Bit-RatesCode0
Bilevel Learning with Inexact Stochastic GradientsCode0
Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image PriorCode0
Manifold Denoising by Nonlinear Robust Principal Component AnalysisCode0
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy TrainingCode0
Magnetogram-to-Magnetogram: Generative Forecasting of Solar EvolutionCode0
Magicremover: Tuning-free Text-guided Image inpainting with Diffusion ModelsCode0
A multimodal dynamical variational autoencoder for audiovisual speech representation learningCode0
MagicPortrait: Temporally Consistent Face Reenactment with 3D Geometric GuidanceCode0
Denoising Diffusion-Based Control of Nonlinear SystemsCode0
A Multilinear Tongue Model Derived from Speech Related MRI Data of the Human Vocal TractCode0
Denoising Deep Generative ModelsCode0
Machine learning based lens-free imaging technique for field-portable cytometryCode0
MambaFoley: Foley Sound Generation using Selective State-Space ModelsCode0
Cross-model Back-translated Distillation for Unsupervised Machine TranslationCode0
Low-Complexity Blind Parameter Estimation in Wireless Systems with Noisy Sparse SignalsCode0
A Multi-Head Convolutional Neural Network With Multi-path Attention improves Image DenoisingCode0
Denoising Bottleneck with Mutual Information Maximization for Video Multimodal FusionCode0
Beyond the Visible: Jointly Attending to Spectral and Spatial Dimensions with HSI-Diffusion for the FINCH SpacecraftCode0
Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging ProblemsCode0
LoFi: Neural Local Fields for Scalable Image ReconstructionCode0
Low Frequency Adversarial PerturbationCode0
LLNet: A Deep Autoencoder Approach to Natural Low-light Image EnhancementCode0
LMD: Faster Image Reconstruction with Latent Masking DiffusionCode0
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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