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 3426–3450 of 7282 papers

TitleStatusHype
HTMD-Net: A Hybrid Masking-Denoising Approach to Time-Domain Monaural Singing Voice Separation—0
Controllable Person Image Synthesis with Pose-Constrained Latent Diffusion—0
TexGen: Text-Guided 3D Texture Generation with Multi-view Sampling and Resampling—0
Enhancing CTR Prediction in Recommendation Domain with Search Query Representation—0
Enhancing Crowdsourced Audio for Text-to-Speech Models—0
Human Video Translation via Query Warping—0
Hume: Introducing System-2 Thinking in Visual-Language-Action Model—0
HunyuanPortrait: Implicit Condition Control for Enhanced Portrait Animation—0
Tencent Text-Video Retrieval: Hierarchical Cross-Modal Interactions with Multi-Level Representations—0
HW-TSC’s Participation in the WMT 2021 Triangular MT Shared Task—0
ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals—0
Hybrid diffusion models: combining supervised and generative pretraining for label-efficient fine-tuning of segmentation models—0
Hybrid Digital-Analog Semantic Communications—0
Improving End-to-end Speech Translation by Leveraging Auxiliary Speech and Text Data—0
Controllable Inversion of Black-Box Face Recognition Models via Diffusion—0
HybridoNet-Adapt: A Domain-Adapted Framework for Accurate Lithium-Ion Battery RUL Prediction—0
Defense against adversarial attacks on deep convolutional neural networks through nonlocal denoising—0
Controllable Human Image Generation with Personalized Multi-Garments—0
Enhancing Black-Litterman Portfolio via Hybrid Forecasting Model Combining Multivariate Decomposition and Noise Reduction—0
HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model—0
Enhancement of the Prefiltered Rotationally Invariant Non-local PCA Algorithm for MRI—0
Adversary-Robust Graph-Based Learning of WSIs—0
Improving Interpretation Faithfulness for Vision Transformers—0
Improving global awareness of linkset predictions using Cross-Attentive Modulation tokens—0
Improving J-divergence of brain connectivity states by graph Laplacian denoising—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