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 3676–3700 of 7282 papers

TitleStatusHype
Stream Query Denoising for Vectorized HD Map Construction—0
3D Human Pose Analysis via Diffusion Synthesis—0
Siamese Meets Diffusion Network: SMDNet for Enhanced Change Detection in High-Resolution RS Imagery—0
Optimizing k in kNN Graphs with Graph Learning Perspective—0
Robust Tiny Object Detection in Aerial Images amidst Label NoiseCode0
Registration of algebraic varieties using Riemannian optimizationCode0
KADEL: Knowledge-Aware Denoising Learning for Commit Message GenerationCode0
ED-TTS: Multi-Scale Emotion Modeling using Cross-Domain Emotion Diarization for Emotional Speech Synthesis—0
EmoTalker: Emotionally Editable Talking Face Generation via Diffusion Model—0
Multi-scale 2D Temporal Map Diffusion Models for Natural Language Video Localization—0
Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation—0
PolMERLIN: Self-Supervised Polarimetric Complex SAR Image Despeckling with Masked Networks—0
Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemble—0
Adaptive Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising and Destriping—0
Frequency-Time Diffusion with Neural Cellular Automata—0
Can Active Label Correction Improve LLM-based Modular AI Systems?—0
Generative AI Meets Semantic Communication: Evolution and Revolution of Communication Tasks—0
Multi-Dimensional Framework for EEG Signal Processing and Denoising Through Tensor-based Architecture—0
A locally statistical active contour model for SAR image segmentation can be solved by denoising algorithms—0
Score Distillation Sampling with Learned Manifold Corrective—0
Representative Feature Extraction During Diffusion Process for Sketch Extraction with One Example—0
Design a Metric Robust to Complicated High Dimensional Noise for Efficient Manifold Denoising—0
Physics-informed Neural Networks for Encoding Dynamics in Real Physical Systems—0
ROIC-DM: Robust Text Inference and Classification via Diffusion Model—0
SAR Despeckling via Regional Denoising Diffusion Probabilistic Model—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