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 576600 of 7282 papers

TitleStatusHype
CT-Mamba: A Hybrid Convolutional State Space Model for Low-Dose CT DenoisingCode1
PocoLoco: A Point Cloud Diffusion Model of Human Shape in Loose ClothingCode1
EDformer: Transformer-Based Event Denoising Across Varied Noise LevelsCode1
Conditional Controllable Image FusionCode1
Diffusion Models as Network Optimizers: Explorations and AnalysisCode1
Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales?Code1
bit2bit: 1-bit quanta video reconstruction via self-supervised photon predictionCode1
Diffusion Priors for Variational Likelihood Estimation and Image DenoisingCode1
TopoDiffusionNet: A Topology-aware Diffusion ModelCode1
TabSeq: A Framework for Deep Learning on Tabular Data via Sequential OrderingCode1
Open Materials 2024 (OMat24) Inorganic Materials Dataset and ModelsCode1
On the Effectiveness of Dataset Alignment for Fake Image DetectionCode1
Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain NavigationCode1
TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory PredictionCode1
CleanUMamba: A Compact Mamba Network for Speech Denoising using Channel PruningCode1
Variational Diffusion Posterior Sampling with Midpoint GuidanceCode1
DiffPO: A causal diffusion model for learning distributions of potential outcomesCode1
CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack SegmentationCode1
InstructG2I: Synthesizing Images from Multimodal Attributed GraphsCode1
Training-free Diffusion Model Alignment with Sampling DemonsCode1
Diffusion Auto-regressive Transformer for Effective Self-supervised Time Series ForecastingCode1
Image Watermarks are Removable Using Controllable Regeneration from Clean NoiseCode1
DiffuseReg: Denoising Diffusion Model for Obtaining Deformation Fields in Unsupervised Deformable Image RegistrationCode1
TrustEMG-Net: Using Representation-Masking Transformer with U-Net for Surface Electromyography EnhancementCode1
SymmetricDiffusers: Learning Discrete Diffusion on Finite Symmetric GroupsCode1
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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