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 351–375 of 7282 papers

TitleStatusHype
Self-Supervised Noise Adaptive MRI Denoising via Repetition to Repetition (Rep2Rep) Learning—0
CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors—0
Fast Autoregressive Models for Continuous Latent Generation—0
A Machine Learning Approach for Denoising and Upsampling HRTFs—0
Evolution Meets Diffusion: Efficient Neural Architecture Generation—0
ECGDeDRDNet: A deep learning-based method for Electrocardiogram noise removal using a double recurrent dense network—0
Diffusion Probabilistic Models for Compressive SAR Imaging—0
Physics-guided and fabrication-aware inverse design of photonic devices using diffusion modelsCode1
Survey of Video Diffusion Models: Foundations, Implementations, and ApplicationsCode1
Self-Controlled Diffusion for Denoising in Scientific Imaging—0
Aerial Active STAR-RIS-assisted Satellite-Terrestrial Covert Communications—0
An Automated Pipeline for Few-Shot Bird Call Classification: A Case Study with the Tooth-Billed PigeonCode0
A Self-supervised Learning Method for Raman Spectroscopy based on Masked Autoencoders—0
Learned Primal Dual Splitting for Self-Supervised Noise-Adaptive MRI Reconstruction—0
DyST-XL: Dynamic Layout Planning and Content Control for Compositional Text-to-Video Generation—0
PIV-FlowDiffuser:Transfer-learning-based denoising diffusion models for PIVCode0
FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models—0
An Artificial Intelligence Enabled Signature Estimation of Dual Wideband Systems in Ultra-Low Signal-to-Noise Ratio—0
Generative emulation of chaotic dynamics with coherent prior—0
Text-Audio-Visual-conditioned Diffusion Model for Video Saliency Prediction—0
Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided CalibrationCode1
OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone InscriptionsCode0
WeatherGen: A Unified Diverse Weather Generator for LiDAR Point Clouds via Spider Mamba DiffusionCode1
Filter2Noise: Interpretable Self-Supervised Single-Image Denoising for Low-Dose CT with Attention-Guided Bilateral FilteringCode1
Adaptive Long-term Embedding with Denoising and Augmentation for RecommendationCode0
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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