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 3076–3100 of 7282 papers

TitleStatusHype
Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising—0
A CT Image Denoising Method Based on Projection Domain Feature—0
GaussianDiffusion: 3D Gaussian Splatting for Denoising Diffusion Probabilistic Models with Structured Noise—0
Guided Speech Enhancement Network—0
H2TF for Hyperspectral Image Denoising: Where Hierarchical Nonlinear Transform Meets Hierarchical Matrix Factorization—0
Hair and Scalp Disease Detection using Machine Learning and Image Processing—0
Gauss-Newton Unrolled Neural Networks and Data-driven Priors for Regularized PSSE with Robustness—0
Gaze-guided Hand-Object Interaction Synthesis: Dataset and Method—0
GazeMoDiff: Gaze-guided Diffusion Model for Stochastic Human Motion Prediction—0
GDTS: Goal-Guided Diffusion Model with Tree Sampling for Multi-Modal Pedestrian Trajectory Prediction—0
HanDrawer: Leveraging Spatial Information to Render Realistic Hands Using a Conditional Diffusion Model in Single Stage—0
HDR Denoising and Deblurring by Learning Spatio-temporal Distortion Models—0
High-Fidelity Diffusion-based Image Editing—0
GECCO: Geometrically-Conditioned Point Diffusion Models—0
Deconstructing Principal Component Analysis Using a Data Reconciliation Perspective—0
GEM3D: GEnerative Medial Abstractions for 3D Shape Synthesis—0
GenCAD-Self-Repairing: Feasibility Enhancement for 3D CAD Generation—0
Estimating High Order Gradients of the Data Distribution by Denoising—0
Estimating Fine-Grained Noise Model via Contrastive Learning—0
Convolutional Bipartite Attractor Networks—0
Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems—0
Convex Variational Image Restoration with Histogram Priors—0
Decoupled Diffusion Sparks Adaptive Scene Generation—0
A Self-supervised Diffusion Bridge for MRI Reconstruction—0
ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration—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