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 2526–2550 of 7282 papers

TitleStatusHype
VJT: A Video Transformer on Joint Tasks of Deblurring, Low-light Enhancement and Denoising—0
Masked Pre-training Enables Universal Zero-shot DenoiserCode1
CascadedGaze: Efficiency in Global Context Extraction for Image RestorationCode2
Deconstructing Denoising Diffusion Models for Self-Supervised LearningCode2
Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for Fine-grained Vehicle RecognitionCode1
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation LearningCode1
FLLIC: Functionally Lossless Image Compression—0
Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach—0
Graph Diffusion Transformers for Multi-Conditional Molecular GenerationCode2
UNIMO-G: Unified Image Generation through Multimodal Conditional Diffusion—0
Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet ExtractionCode1
Dual-Domain Coarse-to-Fine Progressive Estimation Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECTCode1
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
Exploring Diffusion Time-steps for Unsupervised Representation LearningCode1
MotionMix: Weakly-Supervised Diffusion for Controllable Motion GenerationCode1
Product-Level Try-on: Characteristics-preserving Try-on with Realistic Clothes Shading and Wrinkles—0
Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient—0
Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: Autoencoder and Bayesian Neural Network Approaches—0
Large Language Models are Efficient Learners of Noise-Robust Speech RecognitionCode2
Application of Joint Notch Filtering and Wavelet Transform for Enhanced Powerline Interference Removal in Atrial Fibrillation Electrograms—0
Sub2Full: split spectrum to boost OCT despeckling without clean dataCode0
Fast graph-based denoising for point cloud color information—0
Automatic Tuning of Denoising Algorithms Parameters Without Ground TruthCode0
Show:102550
← PrevPage 102 of 292Next →
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