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

Showing 26012625 of 7282 papers

TitleStatusHype
Geometric-Facilitated Denoising Diffusion Model for 3D Molecule GenerationCode0
Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamicsCode0
CNN-Based Real-Time Parameter Tuning for Optimizing Denoising Filter PerformanceCode0
GeomCLIP: Contrastive Geometry-Text Pre-training for MoleculesCode0
Natural Image Noise DatasetCode0
Global Point Cloud Registration Network for Large TransformationsCode0
A distribution-dependent Mumford-Shah model for unsupervised hyperspectral image segmentationCode0
GenPlan: Generative Sequence Models as Adaptive PlannersCode0
GeoGuide: Geometric guidance of diffusion modelsCode0
Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A Simulation StudyCode0
Enhanced Control for Diffusion Bridge in Image RestorationCode0
Enhanced countering adversarial attacks via input denoising and feature restoringCode0
Generative Plug and Play: Posterior Sampling for Inverse ProblemsCode0
A Robust Alternative for Graph Convolutional Neural Networks via Graph Neighborhood FiltersCode0
Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational PosteriorsCode0
Cloud K-SVD for Image DenoisingCode0
Generative Modeling with DiffusionCode0
Cloud Dictionary: Sparse Coding and Modeling for Point CloudsCode0
DiffusionTrack: Point Set Diffusion Model for Visual Object TrackingCode0
Generative Flows as a General Purpose Solution for Inverse ProblemsCode0
Generative Modeling of Microweather Wind Velocities for Urban Air MobilityCode0
Generative Modeling of Seismic Data using Diffusion Models and its Application to Multi-Purpose Seismic Inverse ProblemsCode0
Generative Simulations of The Solar Corona Evolution With Denoising Diffusion : Proof of ConceptCode0
Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based RecommendationCode0
Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified