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 5126–5150 of 7282 papers

TitleStatusHype
Learning to Efficiently Sample from Diffusion Probabilistic Models—0
Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue ConditionCode0
MemStream: Memory-Based Streaming Anomaly DetectionCode1
Uformer: A General U-Shaped Transformer for Image RestorationCode1
Denoising Word Embeddings by Averaging in a Shared Space—0
Neural Architecture Search via Bregman IterationsCode1
Language Embeddings for Typology and Cross-lingual Transfer LearningCode0
ZmBART: An Unsupervised Cross-lingual Transfer Framework for Language GenerationCode1
Semi-supervised Learning with Missing Values Imputation—0
Denoising and Optical and SAR Image Classifications Based on Feature Extraction and Sparse Representation—0
Representation Learning in Continuous-Time Score-Based Generative Models—0
-VAEs : Optimising variational inference by learning data-dependent divergence skew—0
Unsharp Mask Guided FilteringCode1
UETfishes at MEDIQA 2021: Standing-on-the-Shoulders-of-Giants Model for Abstractive Multi-answer Summarization—0
Bilateral Spectrum Weighted Total Variation for Noisy-Image Super-Resolution and Image Denoising—0
HERALD: An Annotation Efficient Method to Detect User Disengagement in Social ConversationsCode0
MalPhase: Fine-Grained Malware Detection Using Network Flow Data—0
Stable and Interpretable Unrolled Dictionary LearningCode0
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
Diffusion-Based Representation Learning—0
Beyond the Spectrum: Detecting Deepfakes via Re-SynthesisCode1
3D U-NetR: Low Dose Computed Tomography Reconstruction via Deep Learning and 3 Dimensional Convolutions—0
On Hamilton-Jacobi PDEs and image denoising models with certain non-additive noiseCode0
DiffSVC: A Diffusion Probabilistic Model for Singing Voice Conversion—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