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 7001–7025 of 7282 papers

TitleStatusHype
Anomaly Detection with Robust Deep AutoencodersCode0
On enhancing the robustness of Vision Transformers: Defensive DiffusionCode0
Butterfly-Net2: Simplified Butterfly-Net and Fourier Transform InitializationCode0
Image Embedding for Denoising Generative ModelsCode0
Towards Effective and Efficient Adversarial Defense with Diffusion Models for Robust Visual TrackingCode0
Image Fusion via Sparse Regularization with Non-Convex PenaltiesCode0
Adaptive 3D descattering with a dynamic synthesis networkCode0
Masked Diffusion with Task-awareness for Procedure Planning in Instructional VideosCode0
Unsupervised Natural Language Generation with Denoising AutoencodersCode0
Image Inpainting via Tractable Steering of Diffusion ModelsCode0
Infrared Image Deturbulence Restoration Using Degradation Parameter-Assisted Wide & Deep LearningCode0
Stable and Interpretable Unrolled Dictionary LearningCode0
Image quality measurements and denoising using Fourier Ring CorrelationsCode0
C2F-TP: A Coarse-to-Fine Denoising Framework for Uncertainty-Aware Trajectory PredictionCode0
Combining a Context Aware Neural Network with a Denoising Autoencoder for Measuring String SimilaritiesCode0
Automatic Tuning of Denoising Algorithms Parameters Without Ground TruthCode0
Mask-GVAE: Blind Denoising Graphs via PartitionCode0
Image Reconstruction with Predictive Filter FlowCode0
End-to-End Denoising of Dark Burst Images Using Recurrent Fully Convolutional NetworksCode0
Unsupervised Neural Text SimplificationCode0
MaskMedPaint: Masked Medical Image Inpainting with Diffusion Models for Mitigation of Spurious CorrelationsCode0
Combining Denoising Autoencoders with Contrastive Learning to fine-tune Transformer ModelsCode0
End-to-End Multi-Task Denoising for joint SDR and PESQ OptimizationCode0
MaskPure: Improving Defense Against Text Adversaries with Stochastic PurificationCode0
Cached Adaptive Token Merging: Dynamic Token Reduction and Redundant Computation Elimination in Diffusion ModelCode0
Show:102550
← PrevPage 281 of 292Next →
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