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 1051–1100 of 7282 papers

TitleStatusHype
Deep Convolutional Denoising of Low-Light ImagesCode1
DDIM sampling for Generative AIBIM, a faster intelligent structural design frameworkCode1
HiFi-GAN: High-Fidelity Denoising and Dereverberation Based on Speech Deep Features in Adversarial NetworksCode1
High-quality Image Dehazing with Diffusion ModelCode1
Homotopic Gradients of Generative Density Priors for MR Image ReconstructionCode1
Hyperspectral image denoising based on global and non-local low-rank factorizationsCode1
Harnessing the Spatial-Temporal Attention of Diffusion Models for High-Fidelity Text-to-Image SynthesisCode1
HarmoniCa: Harmonizing Training and Inference for Better Feature Caching in Diffusion Transformer AccelerationCode1
Guided Diffusion Sampling on Function Spaces with Applications to PDEsCode1
Data-Driven Denoising of Stationary Accelerometer SignalsCode1
Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIsCode1
HD-KT: Advancing Robust Knowledge Tracing via Anomalous Learning Interaction DetectionCode1
Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion ModelsCode1
3D Quasi-Recurrent Neural Network for Hyperspectral Image DenoisingCode1
A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy ImagesCode1
Data-Centric Learning from Unlabeled Graphs with Diffusion ModelCode1
Guided Diffusion Model for Adversarial PurificationCode1
Aerial Height Prediction and Refinement Neural Networks with Semantic and Geometric GuidanceCode1
G-SimCLR: Self-Supervised Contrastive Learning with Guided Projection via Pseudo LabellingCode1
A Physics-based Noise Formation Model for Extreme Low-light Raw DenoisingCode1
Advanced Deep Networks for 3D Mitochondria Instance SegmentationCode1
G-SimCLR : Self-Supervised Contrastive Learning with Guided Projection via Pseudo LabellingCode1
AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot NetworkCode1
AdvAD: Exploring Non-Parametric Diffusion for Imperceptible Adversarial AttacksCode1
DAS-N2N: Machine learning Distributed Acoustic Sensing (DAS) signal denoising without clean dataCode1
3D Priors-Guided Diffusion for Blind Face RestorationCode1
DALE: Generative Data Augmentation for Low-Resource Legal NLPCode1
DCT2net: an interpretable shallow CNN for image denoisingCode1
Guaranteed Tensor Recovery Fused Low-rankness and SmoothnessCode1
Heterogeneous window transformer for image denoisingCode1
D4AM: A General Denoising Framework for Downstream Acoustic ModelsCode1
D3RM: A Discrete Denoising Diffusion Refinement Model for Piano TranscriptionCode1
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising DiffusionCode1
An Unsupervised Deep Learning Approach for Real-World Image DenoisingCode1
Unsupervised Sketch-to-Photo SynthesisCode1
D3A-TS: Denoising-Driven Data Augmentation in Time SeriesCode1
D^4-VTON: Dynamic Semantics Disentangling for Differential Diffusion based Virtual Try-OnCode1
DAEMA: Denoising Autoencoder with Mask AttentionCode1
Dynamic Addition of Noise in a Diffusion Model for Anomaly DetectionCode1
Graph Information Bottleneck for Subgraph RecognitionCode1
D^2-DPM: Dual Denoising for Quantized Diffusion Probabilistic ModelsCode1
D2HNet: Joint Denoising and Deblurring with Hierarchical Network for Robust Night Image RestorationCode1
AdaGNN: Graph Neural Networks with Adaptive Frequency Response FilterCode1
GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world Noise ModelingCode1
CycleISP: Real Image Restoration via Improved Data SynthesisCode1
AdsorbDiff: Adsorbate Placement via Conditional Denoising DiffusionCode1
Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh ReconstructionCode1
D2C: Diffusion-Denoising Models for Few-shot Conditional GenerationCode1
Deep Image PriorCode1
Graph Collaborative Signals Denoising and Augmentation for RecommendationCode1
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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