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 3601–3625 of 7282 papers

TitleStatusHype
Denoising Diffusion Error Correction Codes—0
Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI—0
Implicit neural representations for end-to-end PET reconstruction—0
Implicit Regression in Subspace for High-Sensitivity CEST Imaging—0
Denoising Diffusion for Sampling SAT Solutions—0
Contrastive-Adversarial and Diffusion: Exploring pre-training and fine-tuning strategies for sulcal identification—0
End-to-end Recurrent Denoising Autoencoder Embeddings for Speaker Identification—0
A Review Paper: Noise Models in Digital Image Processing—0
End-to-End Learning for Structured Prediction Energy Networks—0
Improved DDIM Sampling with Moment Matching Gaussian Mixtures—0
Continuous-variable Quantum Diffusion Model for State Generation and Restoration—0
Improved Detection of Adversarial Images Using Deep Neural Networks—0
3D Wasserstein generative adversarial network with dense U-Net based discriminator for preclinical fMRI denoising—0
Detection and Imputation based Two-Stage Denoising Diffusion Power System Measurement Recovery under Cyber-Physical Uncertainties—0
Ising Models with Hidden Markov Structure: Applications to Probabilistic Inference in Machine Learning—0
Improved functional MRI activation mapping in white matter through diffusion-adapted spatial filtering—0
Continuous Optimization for Fields of Experts Denoising Works—0
Denoising diffusion models for high-resolution microscopy image restoration—0
End-to-End Adaptive Monte Carlo Denoising and Super-Resolution—0
Improved Quasi-Recurrent Neural Network for Hyperspectral Image Denoising—0
InSAR Phase Denoising: A Review of Current Technologies and Future Directions—0
Improved Sparse Low-Rank Matrix Estimation—0
END: Early Noise Dropping for Efficient and Effective Context Denoising—0
END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation—0
Continuous Modeling of the Denoising Process for Speech Enhancement Based on Deep Learning—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