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 4976–5000 of 7282 papers

TitleStatusHype
Robust Technique for Representative Volume Element Identification in Noisy Microtomography Images of Porous Materials Based on Pores Morphology and Their Spatial Distribution—0
Robust Time Series Denoising with Learnable Wavelet Packet Transform—0
Robust Training of Neural Networks at Arbitrary Precision and Sparsity—0
Robust Transfer Principal Component Analysis with Rank Constraints—0
Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training—0
Unsupervised Multi-Criteria Adversarial Detection in Deep Image Retrieval—0
ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning—0
Rogue Emitter Detection Using Hybrid Network of Denoising Autoencoder and Deep Metric Learning—0
Accelerating Mobile Edge Generation (MEG) by Constrained Learning—0
ROIC-DM: Robust Text Inference and Classification via Diffusion Model—0
Rolling Diffusion Models—0
ROODI: Reconstructing Occluded Objects with Denoising Inpainters—0
RoPECraft: Training-Free Motion Transfer with Trajectory-Guided RoPE Optimization on Diffusion Transformers—0
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity—0
Unsupervised Neural Machine Translation for English and Manipuri—0
Roto-Translation Invariant Metrics on Position-Orientation Space—0
RAR-U-Net: a Residual Encoder to Attention Decoder by Residual Connections Framework for Spine Segmentation under Noisy Labels—0
RSEND: Retinex-based Squeeze and Excitation Network with Dark Region Detection for Efficient Low Light Image Enhancement—0
RSR-NF: Neural Field Regularization by Static Restoration Priors for Dynamic Imaging—0
RuleAgent: Discovering Rules for Recommendation Denoising with Autonomous Language Agents—0
Rule of Three for Superresolution of Still Images with Applications to Compression and Denoising—0
WTEFNet: Real-Time Low-Light Object Detection for Advanced Driver-Assistance Systems—0
Run-Time Adaptation of Neural Beamforming for Robust Speech Dereverberation and Denoising—0
RViDeformer: Efficient Raw Video Denoising Transformer with a Larger Benchmark Dataset—0
S^2AG-Vid: Enhancing Multi-Motion Alignment in Video Diffusion Models via Spatial and Syntactic Attention-Based Guidance—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