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 1676–1700 of 7282 papers

TitleStatusHype
Adaptive Cohen's Class Time-Frequency Distribution—0
Convolution Type of Metaplectic Cohen's Distribution Time-Frequency Analysis Theory, Method and Technology—0
Sketch2Scene: Automatic Generation of Interactive 3D Game Scenes from User's Casual Sketches—0
Lightweight Video Denoising Using a Classic Bayesian Backbone—0
Bayes-optimal learning of an extensive-width neural network from quadratically many samplesCode0
Learning from Noisy Labels for Long-tailed Data via Optimal Transport—0
L4DR: LiDAR-4DRadar Fusion for Weather-Robust 3D Object DetectionCode2
Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models—0
TALE: Training-free Cross-domain Image Composition via Adaptive Latent Manipulation and Energy-guided Optimization—0
Hybrid diffusion models: combining supervised and generative pretraining for label-efficient fine-tuning of segmentation models—0
Attacks and Defenses for Generative Diffusion Models: A Comprehensive Survey—0
Adversarial Domain Adaptation for Cross-user Activity Recognition Using Diffusion-based Noise-centred LearningCode0
MDT-A2G: Exploring Masked Diffusion Transformers for Co-Speech Gesture Generation—0
Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Models—0
Cascading Refinement Video Denoising with Uncertainty Adaptivity—0
A Two-Stage Progressive Pre-training using Multi-Modal Contrastive Masked Autoencoders—0
On Probabilistic Embeddings in Optimal Dimension Reduction—0
Multi-weather Cross-view Geo-localization Using Denoising Diffusion Models—0
LDFaceNet: Latent Diffusion-based Network for High-Fidelity Deepfake Generation—0
Step Saver: Predicting Minimum Denoising Steps for Diffusion Model Image Generation—0
Landmark-guided Diffusion Model for High-fidelity and Temporally Coherent Talking Head Generation—0
TexGen: Text-Guided 3D Texture Generation with Multi-view Sampling and Resampling—0
CLIP4Sketch: Enhancing Sketch to Mugshot Matching through Dataset Augmentation using Diffusion Models—0
Stop-and-go wave super-resolution reconstruction via iterative refinement—0
Diff3DETR:Agent-based Diffusion Model for Semi-supervised 3D Object Detection—0
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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