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 3651–3675 of 7282 papers

TitleStatusHype
Signal Quality Auditing for Time-series Data—0
Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman SpectraCode0
Spatial-and-Frequency-aware Restoration method for Images based on Diffusion Models—0
Diffusion Model Compression for Image-to-Image Translation—0
Spatial-Aware Latent Initialization for Controllable Image Generation—0
Sliced Wasserstein with Random-Path Projecting DirectionsCode0
EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm—0
Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization—0
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation—0
Data-Driven Estimation of the False Positive Rate of the Bayes Binary Classifier via Soft Labels—0
VJT: A Video Transformer on Joint Tasks of Deblurring, Low-light Enhancement and Denoising—0
Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach—0
FLLIC: Functionally Lossless Image Compression—0
UNIMO-G: Unified Image Generation through Multimodal Conditional Diffusion—0
TD^2-Net: Toward Denoising and Debiasing for Dynamic Scene Graph Generation—0
LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation LearningCode0
Feature Denoising Diffusion Model for Blind Image Quality Assessment—0
Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient—0
Product-Level Try-on: Characteristics-preserving Try-on with Realistic Clothes Shading and Wrinkles—0
Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: Autoencoder and Bayesian Neural Network Approaches—0
Application of Joint Notch Filtering and Wavelet Transform for Enhanced Powerline Interference Removal in Atrial Fibrillation Electrograms—0
Fast graph-based denoising for point cloud color information—0
Motion-Zero: Zero-Shot Moving Object Control Framework for Diffusion-Based Video Generation—0
Automatic Tuning of Denoising Algorithms Parameters Without Ground TruthCode0
Sub2Full: split spectrum to boost OCT despeckling without clean dataCode0
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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