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 4551–4600 of 7282 papers

TitleStatusHype
Joint Demosaicking and Denoising in the Wild: The Case of Training Under Ground Truth Uncertainty—0
Wasserstein Training of Restricted Boltzmann Machines—0
Joint denoising and distortion correction of atomic scale scanning transmission electron microscopy images—0
Joint Denoising and Few-angle Reconstruction for Low-dose Cardiac SPECT Using a Dual-domain Iterative Network with Adaptive Data Consistency—0
Joint denoising and HDR for RAW video sequences—0
Joint Denoising / Compression of Image Contours via Shape Prior and Context Tree—0
Joint Denoising of Cryo-EM Projection Images using Polar Transformers—0
Joint End-to-End Image Compression and Denoising: Leveraging Contrastive Learning and Multi-Scale Self-ONNs—0
Joint Flow And Feature Refinement Using Attention For Video Restoration—0
The Uncanny Valley: A Comprehensive Analysis of Diffusion Models—0
The Wasserstein transform—0
Diffusion Models for Safety Validation of Autonomous Driving Systems—0
Joint Image Compression and Denoising via Latent-Space Scalability—0
Joint Localization and Planning using Diffusion—0
Thompson Sampling with Diffusion Generative Prior—0
Jointly optimal dereverberation and beamforming—0
Watch and Learn: Leveraging Expert Knowledge and Language for Surgical Video Understanding—0
Joint multiband deconvolution for Euclid and Vera C. Rubin images—0
Three dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks—0
An Interpretable Joint Nonnegative Matrix Factorization-Based Point Cloud Distance Measure—0
Joint Optical Neuroimaging Denoising with Semantic Tasks—0
Joint Prediction and Denoising for Large-scale Multilingual Self-supervised Learning—0
Joint Reconstruction and Calibration using Regularization by Denoising—0
Three-dimensional Optical Coherence Tomography Image Denoising through Multi-input Fully-Convolutional Networks—0
Joint Time-Vertex Fractional Fourier Transform—0
Joint tone mapping and denoising of thermal infrared images via multi-scale Retinex and multi-task learning—0
Three-dimensional spike localization and improved motion correction for Neuropixels recordings—0
JPEG Information Regularized Deep Image Prior for Denoising—0
JSRNN: Joint Sampling and Reconstruction Neural Networks for High Quality Image Compressed Sensing—0
Three-quarter Sibling Regression for Denoising Observational Data—0
Kalman Filter and Wavelet Cross-correlation for VHF Broadband Interferometer Lightning Mapping—0
KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems—0
A Denoising View of Matrix Completion—0
Keep Your Bearings: Lightly-Supervised Information Extraction with Ladder Networks That Avoids Semantic Drift—0
TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance—0
Kernel Estimation from Salient Structure for Robust Motion Deblurring—0
Kernel-predicting convolutional networks for denoising monte carlo renderings.—0
Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study—0
Kernel spectral joint embeddings for high-dimensional noisy datasets using duo-landmark integral operators—0
KNN Local Attention for Image Restoration—0
Knock-Knock: Acoustic Object Recognition by using Stacked Denoising Autoencoders—0
KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP—0
Knowledge Capture and Replay for Continual Learning—0
Thunder: Thumbnail based Fast Lightweight Image Denoising Network—0
Knowledge Distillation for Speech Denoising by Latent Representation Alignment with Cosine Distance—0
A Poisson-Guided Decomposition Network for Extreme Low-Light Image Enhancement—0
TIDE : Temporal-Aware Sparse Autoencoders for Interpretable Diffusion Transformers in Image Generation—0
KOALA: Empirical Lessons Toward Memory-Efficient and Fast Diffusion Models for Text-to-Image Synthesis—0
KoPA: Automated Kronecker Product Approximation—0
KRNET: Image Denoising with Kernel Regulation Network—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