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 2426–2450 of 7282 papers

TitleStatusHype
Diffusion Model Based Visual Compensation Guidance and Visual Difference Analysis for No-Reference Image Quality AssessmentCode1
Cameras as Rays: Pose Estimation via Ray DiffusionCode4
Mel-FullSubNet: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR—0
RFI-DRUnet: Restoring dynamic spectra corrupted by radio frequency interference -- Application to pulsar observations—0
SRNDiff: Short-term Rainfall Nowcasting with Condition Diffusion ModelCode1
A unified framework of non-local parametric methods for image denoising—0
Broadening Target Distributions for Accelerated Diffusion Models via a Novel Analysis Approach—0
DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging LoadCode1
Hybrid Training of Denoising Networks to Improve the Texture Acutance of Digital Cameras—0
RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion ModelsCode2
Denoising OCT Images Using Steered Mixture of Experts with Multi-Model Inference—0
An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning—0
Transformer-based Learned Image Compression for Joint Decoding and Denoising—0
The Uncanny Valley: A Comprehensive Analysis of Diffusion Models—0
Visual Style Prompting with Swapping Self-AttentionCode3
On the Semantic Latent Space of Diffusion-Based Text-to-Speech Models—0
Spatio-Temporal Few-Shot Learning via Diffusive Neural Network GenerationCode2
Regularization by denoising: Bayesian model and Langevin-within-split Gibbs sampling—0
Human Video Translation via Query Warping—0
3D Diffuser Actor: Policy Diffusion with 3D Scene RepresentationsCode3
Image Denoising with Machine Learning: A Novel Approach to Improve Quantum Image Processing Quality and Reliability—0
Learning by Reconstruction Produces Uninformative Features For Perception—0
Where is the answer? Investigating Positional Bias in Language Model Knowledge ExtractionCode0
Stochastic Localization via Iterative Posterior SamplingCode1
3D Diffuser Actor: Policy Diffusion with 3D Scene RepresentationsCode3
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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