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

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Showing 6451–6475 of 7282 papers

TitleStatusHype
DiffSurf: A Transformer-based Diffusion Model for Generating and Reconstructing 3D Surfaces in Pose—0
DiffSVC: A Diffusion Probabilistic Model for Singing Voice Conversion—0
Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data—0
Statistical Denoising for single molecule fluorescence microscopic images—0
Diff-TTS: A Denoising Diffusion Model for Text-to-Speech—0
Diff-TTSG: Denoising probabilistic integrated speech and gesture synthesis—0
DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model—0
A Massive MIMO Sampling Detection Strategy Based on Denoising Diffusion Model—0
Diffuman4D: 4D Consistent Human View Synthesis from Sparse-View Videos with Spatio-Temporal Diffusion Models—0
Statistical Estimation: From Denoising to Sparse Regression and Hidden Cliques—0
Statistical evaluation of visual quality metrics for image denoising—0
DifFUSER: Diffusion Model for Robust Multi-Sensor Fusion in 3D Object Detection and BEV Segmentation—0
DiffusER: Discrete Diffusion via Edit-based Reconstruction—0
Statistical guarantees for denoising reflected diffusion models—0
Diffusing More Objects for Semi-Supervised Domain Adaptation with Less Labeling—0
A Mask-Based Adversarial Defense Scheme—0
A Magnetic Framelet-Based Convolutional Neural Network for Directed Graphs—0
Statistical Inference Using Mean Shift Denoising—0
A Machine Learning Imaging Core using Separable FIR-IIR Filters—0
Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation—0
Statistical limits of dictionary learning: random matrix theory and the spectral replica method—0
DiffusionAttacker: Diffusion-Driven Prompt Manipulation for LLM Jailbreak—0
st-DTPM: Spatial-Temporal Guided Diffusion Transformer Probabilistic Model for Delayed Scan PET Image Prediction—0
Diffusion-Based Adversarial Purification for Speaker Verification—0
Imitating the Functionality of Image-to-Image Models Using a Single Example—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