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 1201–1225 of 7282 papers

TitleStatusHype
A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy ImagesCode1
Denoising Diffusion Models for 3D Healthy Brain Tissue InpaintingCode1
CLEARER: Multi-Scale Neural Architecture Search for Image RestorationCode1
3D Quasi-Recurrent Neural Network for Hyperspectral Image DenoisingCode1
Denoising Adversarial AutoencodersCode1
Denoising and Prompt-Tuning for Multi-Behavior RecommendationCode1
Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image DenoisingCode1
CleanUMamba: A Compact Mamba Network for Speech Denoising using Channel PruningCode1
Anomaly Detection using Score-based Perturbation ResilienceCode1
Denoising Auto-encoding Priors in Undecimated Wavelet Domain for MR Image ReconstructionCode1
D^4-VTON: Dynamic Semantics Disentangling for Differential Diffusion based Virtual Try-OnCode1
Knowledge Diffusion for DistillationCode1
Denoising Diffusion Autoencoders are Unified Self-supervised LearnersCode1
Denoising diffusion-based MRI to CT image translation enables automated spinal segmentationCode1
D3RM: A Discrete Denoising Diffusion Refinement Model for Piano TranscriptionCode1
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximationCode1
D4AM: A General Denoising Framework for Downstream Acoustic ModelsCode1
Denoising Diffusion Probabilistic Model for Retinal Image Generation and SegmentationCode1
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising DiffusionCode1
Computing Multiple Image Reconstructions with a Single HypernetworkCode1
Denoising Diffusion Probabilistic Models for Generation of Realistic Fully-Annotated Microscopy Image Data SetsCode1
Denoising Diffusion Probabilistic Models to Predict the Density of Molecular CloudsCode1
Modeling State Shifting via Local-Global Distillation for Event-Frame Gaze TrackingCode1
Denoising Diffusion Step-aware ModelsCode1
D3A-TS: Denoising-Driven Data Augmentation in Time SeriesCode1
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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