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 7076–7100 of 7282 papers

TitleStatusHype
Synthesizing Realistic Image Restoration Training Pairs: A Diffusion Approach—0
Fast Monte Carlo Tree Diffusion: 100x Speedup via Parallel Sparse Planning—0
Fast Multi-Layer Laplacian Enhancement—0
Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation—0
Fast, nonlocal and neural: a lightweight high quality solution to image denoising—0
Fast Patch-Based Denoising Using Approximated Patch Geodesic Paths—0
Fast Sampling generative model for Ultrasound image reconstruction—0
Fast Scalable Image Restoration using Total Variation Priors and Expectation Propagation—0
Fast Separable Non-Local Means—0
FastSTI: A Fast Conditional Pseudo Numerical Diffusion Model for Spatio-temporal Traffic Data Imputation—0
Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit—0
Fast Sub-millimeter Diffusion MRI using gSlider-SMS and SNR-Enhancing Joint Reconstruction—0
Fast, Trainable, Multiscale Denoising—0
Fast Training of Diffusion Transformer with Extreme Masking for 3D Point Clouds Generation—0
Fast unsupervised Bayesian image segmentation with adaptive spatial regularisation—0
Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models—0
Fast Unsupervised Tensor Restoration via Low-rank Deconvolution—0
Fast Window-Based Event Denoising with Spatiotemporal Correlation Enhancement—0
Fault Tolerant FPGA Implementation on Redundancy Techniques and ECG Denoising—0
Synthesizing realistic sand assemblies with denoising diffusion in latent space—0
FB-MSTCN: A Full-Band Single-Channel Speech Enhancement Method Based on Multi-Scale Temporal Convolutional Network—0
FDiff-Fusion:Denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation—0
FDNet: Frequency Domain Denoising Network For Cell Segmentation in Astrocytes Derived From Induced Pluripotent Stem Cells—0
FDS: Frequency-Aware Denoising Score for Text-Guided Latent Diffusion Image Editing—0
VisioBlend: Sketch and Stroke-Guided Denoising Diffusion Probabilistic Model for Realistic Image Generation—0
Show:102550
← PrevPage 284 of 292Next →
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