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 2551–2575 of 7282 papers

TitleStatusHype
Motion-Zero: Zero-Shot Moving Object Control Framework for Diffusion-Based Video Generation—0
Stream Query Denoising for Vectorized HD Map Construction—0
Siamese Meets Diffusion Network: SMDNet for Enhanced Change Detection in High-Resolution RS Imagery—0
3D Human Pose Analysis via Diffusion Synthesis—0
RoHM: Robust Human Motion Reconstruction via DiffusionCode3
ED-TTS: Multi-Scale Emotion Modeling using Cross-Domain Emotion Diarization for Emotional Speech Synthesis—0
Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation—0
Fixed Point Diffusion ModelsCode2
Registration of algebraic varieties using Riemannian optimizationCode0
Robust Tiny Object Detection in Aerial Images amidst Label NoiseCode0
EmoTalker: Emotionally Editable Talking Face Generation via Diffusion Model—0
Multi-scale 2D Temporal Map Diffusion Models for Natural Language Video Localization—0
KADEL: Knowledge-Aware Denoising Learning for Commit Message GenerationCode0
Optimizing k in kNN Graphs with Graph Learning Perspective—0
PolMERLIN: Self-Supervised Polarimetric Complex SAR Image Despeckling with Masked Networks—0
Denoising Diffusion Recommender ModelCode1
Quantum Denoising Diffusion ModelsCode1
Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemble—0
Motion2VecSets: 4D Latent Vector Set Diffusion for Non-rigid Shape Reconstruction and TrackingCode2
FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data GenerationCode1
DiffDA: a Diffusion Model for Weather-scale Data AssimilationCode1
Adaptive Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising and Destriping—0
Frequency-Time Diffusion with Neural Cellular Automata—0
Erasing Undesirable Influence in Diffusion ModelsCode1
A locally statistical active contour model for SAR image segmentation can be solved by denoising algorithms—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