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 2676–2700 of 7282 papers

TitleStatusHype
Making Images from Images: Interleaving Denoising and Transformation—0
PriorDiffusion: Leverage Language Prior in Diffusion Models for Monocular Depth Estimation—0
FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction—0
Classifier-Free Guidance inside the Attraction Basin May Cause MemorizationCode0
Haar-Laplacian for directed graphsCode0
Detecting Visual Triggers in Cannabis Imagery: A CLIP-Based Multi-Labeling Framework with Local-Global Aggregation—0
Foundation Cures Personalization: Recovering Facial Personalized Models' Prompt Consistency—0
J-Invariant Volume Shuffle for Self-Supervised Cryo-Electron Tomogram Denoising on Single Noisy Volume—0
High-Resolution Image Synthesis via Next-Token Prediction—0
Prioritize Denoising Steps on Diffusion Model Preference Alignment via Explicit Denoised Distribution Estimation—0
Point Cloud Denoising With Fine-Granularity Dynamic Graph Convolutional Networks—0
TaQ-DiT: Time-aware Quantization for Diffusion Transformers—0
Enhancing Medical Image Segmentation with Deep Learning and Diffusion Models—0
Test-Time Adaptation of 3D Point Clouds via Denoising Diffusion Models—0
Point Cloud Resampling with Learnable Heat Diffusion—0
Novel View Extrapolation with Video Diffusion Priors—0
Multitask Learning for SAR Ship Detection with Gaussian-Mask Joint Segmentation—0
Analysis and Synthesis Denoisers for Forward-Backward Plug-and-Play Algorithms—0
DAGSM: Disentangled Avatar Generation with GS-enhanced Mesh—0
EEG Signal Denoising Using pix2pix GAN: Enhancing Neurological Data Analysis—0
A Neural Denoising Vocoder for Clean Waveform Generation from Noisy Mel-Spectrogram based on Amplitude and Phase Predictions—0
A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant informationCode0
mDAE : modified Denoising AutoEncoder for missing data imputation—0
Frequency-Aware Guidance for Blind Image Restoration via Diffusion Models—0
Robust multi-coil MRI reconstruction via self-supervised denoisingCode0
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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