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 2051–2100 of 7282 papers

TitleStatusHype
Learning Pixel-Distribution Prior with Wider Convolution for Image DenoisingCode0
Learning Priors in High-frequency Domain for Inverse Imaging ReconstructionCode0
Learning of Patch-Based Smooth-Plus-Sparse Models for Image ReconstructionCode0
Learning parametric dictionaries for graph signalsCode0
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging ProblemsCode0
Deep learning cardiac motion analysis for human survival predictionCode0
Learning Joint Denoising, Demosaicing, and Compression from the Raw Natural Image Noise DatasetCode0
Learning normalized image densities via dual score matchingCode0
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving AugmentationCode0
Learning to Assimilate in Chaotic Dynamical SystemsCode0
LED: A Large-scale Real-world Paired Dataset for Event Camera DenoisingCode0
Learning Generalizable 3D Manipulation With 10 DemonstrationsCode0
Deep Learning based Switching Filter for Impulsive Noise Removal in Color ImagesCode0
Learning Generative Models using Denoising Density EstimatorsCode0
Learning Equations from Biological Data with Limited Time SamplesCode0
Learning Deep Representations Using Convolutional Auto-encoders with Symmetric Skip ConnectionsCode0
Learning Dynamics of Linear Denoising AutoencodersCode0
Noisy Batch Active Learning with Deterministic AnnealingCode0
Deep learning-based deconvolution for interferometric radio transient reconstructionCode0
Deep Learning-Based Channel EstimationCode0
Automatic Tuning of Denoising Algorithms Parameters Without Ground TruthCode0
Learning-Based Reconstruction of FRI SignalsCode0
Learning Better Masking for Better Language Model Pre-trainingCode0
Learning Deep CNN Denoiser Prior for Image RestorationCode0
Deep Contrastive Patch-Based Subspace Learning for Camera Image Signal ProcessingCode0
Learning in Deep Factor Graphs with Gaussian Belief PropagationCode0
Layered Rendering Diffusion Model for Controllable Zero-Shot Image SynthesisCode0
Lateral Connections in Denoising Autoencoders Support Supervised LearningCode0
DeepISP: Towards Learning an End-to-End Image Processing PipelineCode0
Automatic Online Multi-Source Domain AdaptationCode0
Unsupervised dynamic modeling of medical image transformationCode0
Educating Text Autoencoders: Latent Representation Guidance via DenoisingCode0
Accelerated Cardiac Parametric Mapping using Deep Learning-Refined Subspace ModelsCode0
Learned Convolutional Sparse CodingCode0
Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising NetworksCode0
Large Graph Signal Denoising with Application to Differential PrivacyCode0
Deep Hyperspectral Prior: Denoising, Inpainting, Super-ResolutionCode0
Algorithmic Guarantees for Inverse Imaging with Untrained Network PriorsCode0
Deep Graph Laplacian Regularization for Robust Denoising of Real ImagesCode0
Deep Graph-Convolutional Image DenoisingCode0
Language-Guided Diffusion Model for Visual GroundingCode0
Language Model Preference Evaluation with Multiple Weak EvaluatorsCode0
Learned D-AMP: Principled Neural Network based Compressive Image RecoveryCode0
Learning Instance-Specific Parameters of Black-Box Models Using Differentiable SurrogatesCode0
Labeling, Cutting, Grouping: an Efficient Text Line Segmentation Method for Medieval ManuscriptsCode0
Deep Gaussian Denoiser Epistemic Uncertainty and Decoupled Dual-Attention FusionCode0
Resurrecting Label Propagation for Graphs with Heterophily and Label NoiseCode0
Multi-scale Processing of Noisy Images using Edge Preservation LossesCode0
Convolutional dictionary learning based auto-encoders for natural exponential-family distributionsCode0
k-Sparse AutoencodersCode0
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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