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 2651–2700 of 7282 papers

TitleStatusHype
Generative Modeling with DiffusionCode0
Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A Simulation StudyCode0
Generative Modeling of Microweather Wind Velocities for Urban Air MobilityCode0
Generative Flows as a General Purpose Solution for Inverse ProblemsCode0
Generative Modeling of Seismic Data using Diffusion Models and its Application to Multi-Purpose Seismic Inverse ProblemsCode0
Anomaly Detection with Robust Deep AutoencodersCode0
Generative Plug and Play: Posterior Sampling for Inverse ProblemsCode0
Adaptive Multi-step Refinement Network for Robust Point Cloud RegistrationCode0
Generating observation guided ensembles for data assimilation with denoising diffusion probabilistic modelCode0
Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-DecodingCode0
A Diffusion Model for Event Skeleton GenerationCode0
Generating symbolic music using diffusion modelsCode0
A comparative study between paired and unpaired Image Quality Assessment in Low-Dose CT DenoisingCode0
Anomaly Detection and Prototype Selection Using Polyhedron CurvatureCode0
Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T TrackingCode0
Generalized Robust Fundus Photography-based Vision Loss Estimation for High MyopiaCode0
Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot LearningCode0
Generating Training Data for Denoising Real RGB Images via Camera Pipeline SimulationCode0
GeoGuide: Geometric guidance of diffusion modelsCode0
Diffusion Models with Deterministic Normalizing Flow PriorsCode0
Generalized Denoising Auto-Encoders as Generative ModelsCode0
Diffusion models under low-noise regimeCode0
Generalized Deep Image to Image RegressionCode0
Diffusion Models Meet Network Management: Improving Traffic Matrix Analysis with Diffusion-based ApproachCode0
Generalization through variance: how noise shapes inductive biases in diffusion modelsCode0
Generalized Laplacian Regularized Framelet Graph Neural NetworksCode0
Classifier-Free Guidance inside the Attraction Basin May Cause MemorizationCode0
Classifier and Exemplar Synthesis for Zero-Shot LearningCode0
GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and DecodingCode0
Gated Texture CNN for Efficient and Configurable Image DenoisingCode0
Gated Orthogonal Recurrent Units: On Learning to ForgetCode0
Gaussian Gated Linear NetworksCode0
Annotated Biomedical Video Generation using Denoising Diffusion Probabilistic Models and Flow FieldsCode0
Diffusion Models for Computational Neuroimaging: A SurveyCode0
Class-Aware Fully-Convolutional Gaussian and Poisson DenoisingCode0
MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient NetworkCode0
GanLM: Encoder-Decoder Pre-training with an Auxiliary DiscriminatorCode0
Diffusion Models as Masked AutoencodersCode0
Game Theory for Adversarial Attacks and DefensesCode0
A Dictionary Based Approach for Removing Out-of-Focus BlurCode0
Convolutional Dictionary Learning: Acceleration and ConvergenceCode0
Fully Unsupervised Probabilistic Noise2VoidCode0
A Self-Supervised Method for Attenuating Seismic Random and Tracewise Coherent Noise under the Non-Pixelwise Independence AssumptionCode0
Multi-head Sequence Tagging Model for Grammatical Error CorrectionCode0
A Self-Training Framework Based on Multi-Scale Attention Fusion for Weakly Supervised Semantic SegmentationCode0
Multi-level Wavelet-CNN for Image RestorationCode0
Fuse Your Latents: Video Editing with Multi-source Latent Diffusion ModelsCode0
Evaluating Unsupervised Denoising Requires Unsupervised MetricsCode0
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy ImagesCode0
Generalized Octave Convolutions for Learned Multi-Frequency Image CompressionCode0
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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