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 6851–6875 of 7282 papers

TitleStatusHype
E-MD3C: Taming Masked Diffusion Transformers for Efficient Zero-Shot Object Customization—0
Generative Imaging and Image Processing via Generative Encoder—0
EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks—0
EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluation—0
Emergency Communication: OTFS-Based Semantic Transmission with Diffusion Noise Suppression—0
Supervised Score-Based Modeling by Gradient Boosting—0
EmoDiff: Intensity Controllable Emotional Text-to-Speech with Soft-Label Guidance—0
EMoG: Synthesizing Emotive Co-speech 3D Gesture with Diffusion Model—0
EmoTalker: Emotionally Editable Talking Face Generation via Diffusion Model—0
Empirical Bayesian image restoration by Langevin sampling with a denoising diffusion implicit prior—0
Empirical robustification of pre-trained classifiers—0
Enabling Local Editing in Diffusion Models by Joint and Individual Component Analysis—0
Distributed Machine-Learning for Early HARQ Feedback Prediction in Cloud RANs—0
Encapsulated Composition of Text-to-Image and Text-to-Video Models for High-Quality Video Synthesis—0
Encoding in the Dark Grand Challenge: An Overview—0
END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation—0
END: Early Noise Dropping for Efficient and Effective Context Denoising—0
End-to-End Adaptive Monte Carlo Denoising and Super-Resolution—0
Supervised topological data analysis for MALDI mass spectrometry imaging applications—0
Supplementary Meta-Learning: Towards a Dynamic Model for Deep Neural Networks—0
End-to-End Learning for Structured Prediction Energy Networks—0
End-to-end Recurrent Denoising Autoencoder Embeddings for Speaker Identification—0
SURE-fuse WFF: A Multi-resolution Windowed Fourier Analysis for Interferometric Phase Denoising—0
End-to-end Triple-domain PET Enhancement: A Hybrid Denoising-and-reconstruction Framework for Reconstructing Standard-dose PET Images from Low-dose PET Sinograms—0
End-to-End Unsupervised Document Image Blind 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