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 4076–4100 of 7282 papers

TitleStatusHype
Gradual Training Method for Denoising Auto Encoders—0
Gradual training of deep denoising auto encoders—0
Graffe: Graph Representation Learning via Diffusion Probabilistic Models—0
GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects—0
Graph-based denoising for time-varying point clouds—0
Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement—0
Graph-Based Manifold Frequency Analysis for Denoising—0
Graph Based Sinogram Denoising for Tomographic Reconstructions—0
Temporal and volumetric denoising via quantile sparse image prior—0
Graph Chirp Signal and Graph Fractional Vertex-Frequency Energy Distribution—0
VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix—0
Graph Convolutional Neural Networks for Automated Echocardiography View Recognition: A Holistic Approach—0
Graph Defense Diffusion Model—0
Temporal Autoencoding Improves Generative Models of Time Series—0
Graph Differentiable Architecture Search with Structure Learning—0
Graph Feature Gating Networks—0
Graph filtering over expanding graphs—0
Graph Generation via Spectral Diffusion—0
Graphics2RAW: Mapping Computer Graphics Images to Sensor RAW Images—0
Temporal evolution of the Covid19 pandemic reproduction number: Estimations from proximal optimization to Monte Carlo sampling—0
Graph Laplacian Regularization for Image Denoising: Analysis in the Continuous Domain—0
Graph Neural Networks and Differential Equations: A hybrid approach for data assimilation of fluid flows—0
3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network—0
Graph Representation Learning with Diffusion Generative Models—0
Graph Sanitation with Application to Node Classification—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