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 5401–5425 of 7282 papers

TitleStatusHype
Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue ConditionCode0
Learning to Efficiently Sample from Diffusion Probabilistic Models—0
Denoising Word Embeddings by Averaging in a Shared Space—0
Language Embeddings for Typology and Cross-lingual Transfer LearningCode0
Denoising and Optical and SAR Image Classifications Based on Feature Extraction and Sparse Representation—0
Semi-supervised Learning with Missing Values Imputation—0
Representation Learning in Continuous-Time Score-Based Generative Models—0
-VAEs : Optimising variational inference by learning data-dependent divergence skew—0
UETfishes at MEDIQA 2021: Standing-on-the-Shoulders-of-Giants Model for Abstractive Multi-answer Summarization—0
HERALD: An Annotation Efficient Method to Detect User Disengagement in Social ConversationsCode0
MalPhase: Fine-Grained Malware Detection Using Network Flow Data—0
Bilateral Spectrum Weighted Total Variation for Noisy-Image Super-Resolution and Image Denoising—0
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel DataCode0
Low-Dose CT Denoising Using a Structure-Preserving Kernel Prediction Network—0
Stable and Interpretable Unrolled Dictionary LearningCode0
Diffusion-Based Representation Learning—0
3D U-NetR: Low Dose Computed Tomography Reconstruction via Deep Learning and 3 Dimensional Convolutions—0
DiffSVC: A Diffusion Probabilistic Model for Singing Voice Conversion—0
On Hamilton-Jacobi PDEs and image denoising models with certain non-additive noiseCode0
HDRUNet: Single Image HDR Reconstruction with Denoising and DequantizationCode0
MR elasticity reconstruction using statistical physical modeling and explicit data-driven denoising regularizer—0
Probabilistic Selective Encryption of Convolutional Neural Networks for Hierarchical Services—0
Learning a Model-Driven Variational Network for Deformable Image Registration—0
Gated Fusion Network for SAO Filter and Inter Frame Prediction in Versatile Video Coding—0
High-Frequency aware Perceptual Image Enhancement—0
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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