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 3601–3625 of 7282 papers

TitleStatusHype
A unified framework of non-local parametric methods for image denoising—0
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
The Uncanny Valley: A Comprehensive Analysis of Diffusion Models—0
Denoising OCT Images Using Steered Mixture of Experts with Multi-Model Inference—0
Hybrid Training of Denoising Networks to Improve the Texture Acutance of Digital Cameras—0
Transformer-based Learned Image Compression for Joint Decoding and Denoising—0
An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning—0
Human Video Translation via Query Warping—0
On the Semantic Latent Space of Diffusion-Based Text-to-Speech Models—0
Regularization by denoising: Bayesian model and Langevin-within-split Gibbs sampling—0
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
Speaking in Wavelet Domain: A Simple and Efficient Approach to Speed up Speech Diffusion Model—0
Classification Diffusion Models: Revitalizing Density Ratio Estimation—0
DestripeCycleGAN: Stripe Simulation CycleGAN for Unsupervised Infrared Image DestripingCode0
Fast Window-Based Event Denoising with Spatiotemporal Correlation Enhancement—0
Patch-based adaptive temporal filter and residual evaluation—0
Denoising Diffusion Restoration Tackles Forward and Inverse Problems for the Laplace Operator—0
Benchmarking multi-component signal processing methods in the time-frequency planeCode0
PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models—0
PFCM: Poisson flow consistency models for low-dose CT image denoising—0
Target Score Matching—0
Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion modelsCode0
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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