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 5301–5350 of 7282 papers

TitleStatusHype
Adaptive Direction-Guided Structure Tensor Total Variation—0
Tree-based iterated local search for Markov random fields with applications in image analysis—0
Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation—0
TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models—0
Noise Distribution Adaptive Self-Supervised Image Denoising using Tweedie Distribution and Score Matching—0
Noise Estimation for Generative Diffusion Models—0
TriDo-Former: A Triple-Domain Transformer for Direct PET Reconstruction from Low-Dose Sinograms—0
Noise in Structured-Light Stereo Depth Cameras: Modeling and its Applications—0
Noise Learning Based Denoising Autoencoder—0
Noise-Level Estimation from Single Color Image Using Correlations Between Textures in RGB Channels—0
Adaptive diffusion constrained total variation scheme with application to `cartoon + texture + edge' image decomposition—0
Noise propagation and MP-PCA image denoising for high-resolution quantitative T2* and magnetic susceptibility mapping (QSM)—0
Noise Reconstruction and Removal Network: A New Way to Denoise FIB-SEM Images—0
Noise reduction in ISAR imaging of UAVs using weighted atomic norm minimization and 2D-ADMM algorithm—0
Noise Reduction to Compute Tissue Mineral Density and Trabecular Bone Volume Fraction from Low Resolution QCT—0
Noise-Robust Target-Speaker Voice Activity Detection Through Self-Supervised Pretraining—0
Noise-specific denoising method with applications to high-frequency ultrasonic images—0
Noise Synthesis for Low-Light Image Denoising with Diffusion Models—0
NoiseTrans: Point Cloud Denoising with Transformers—0
Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction—0
Non-asymptotic bounds for forward processes in denoising diffusions: Ornstein-Uhlenbeck is hard to beat—0
Broadening Target Distributions for Accelerated Diffusion Models via a Novel Analysis Approach—0
Non-autoregressive Conditional Diffusion Models for Time Series Prediction—0
Non-Autoregressive Diffusion-based Temporal Point Processes for Continuous-Time Long-Term Event Prediction—0
Non-Contact Heart Rate Measurement from Deteriorated Videos—0
Nonconvex Approach for Sparse and Low-Rank Constrained Models with Dual Momentum—0
T-SVD Based Non-convex Tensor Completion and Robust Principal Component Analysis—0
Non-Convex Weighted Lp Minimization based Group Sparse Representation Framework for Image Denoising—0
Non-Denoising Forward-Time Diffusions—0
DDAE++: Enhancing Diffusion Models Towards Unified Generative and Discriminative Learning—0
Nonlinear Denoising, Linear Demixing—0
Nonlinear denoising score matching for enhanced learning of structured distributions—0
Nonlinear Filter Based Image Denoising Using AMF Approach—0
Nonlinear Inverse Design of Mechanical Multi-Material Metamaterials Enabled by Video Denoising Diffusion and Structure Identifier—0
Non-linear prediction with LSTM recurrent neural networks for acoustic novelty detection—0
Non-line-of-sight reconstruction via structure sparsity regularization—0
Non-Local Color Image Denoising with Convolutional Neural Networks—0
TruePose: Human-Parsing-guided Attention Diffusion for Full-ID Preserving Pose Transfer—0
Truncated Consistency Models—0
Non-Local Part-Aware Point Cloud Denoising—0
Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations—0
Nonperfused Retinal Capillaries -- A New Method Developed on OCT and OCTA—0
Non-rigid Motion Correction for MRI Reconstruction via Coarse-To-Fine Diffusion Models—0
Adaptive Denoising via GainTuning—0
Nonsymbolic Text Representation—0
Nonsymbolic Text Representation—0
Non-Uniform Diffusion Models—0
Non-Uniform Exposure Imaging via Neuromorphic Shutter Control—0
A Correlative Denoising Autoencoder to Model Social Influence for Top-N Recommender System—0
No-reference denoising of low-dose CT projections—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