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 3326–3350 of 7282 papers

TitleStatusHype
Ultrasonic Image's Annotation Removal: A Self-supervised Noise2Noise ApproachCode0
Seismic Data Interpolation via Denoising Diffusion Implicit Models with Coherence-corrected Resampling—0
Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and EnsemblingCode1
A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation from Wearables—0
Unsupervised 3D out-of-distribution detection with latent diffusion modelsCode1
Hyperspectral and Multispectral Image Fusion Using the Conditional Denoising Diffusion Probabilistic ModelCode1
ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images—0
Application of Spherical Convolutional Neural Networks to Image Reconstruction and Denoising in Nuclear Medicine—0
Undecimated Wavelet Transform for Word Embedded Semantic Marginal Autoencoder in Security improvement and Denoising different Languages—0
Single Image LDR to HDR Conversion using Conditional Diffusion—0
PanoDiffusion: 360-degree Panorama Outpainting via Diffusion—0
Recovering implicit pitch contours from formants in whispered speech—0
Deep Speech Synthesis from MRI-Based Articulatory RepresentationsCode1
Retinex-based Image Denoising / Contrast Enhancement using Gradient Graph Laplacian Regularizer—0
Leveraging Denoised Abstract Meaning Representation for Grammatical Error Correction—0
Physics-assisted Deep Learning for FMCW Radar Quantitative Imaging of Two-dimension Target—0
DiffFlow: A Unified SDE Framework for Score-Based Diffusion Models and Generative Adversarial Networks—0
Focusing on what to decode and what to train: SOV Decoding with Specific Target Guided DeNoising and Vision Language AdvisorCode0
LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse DiffusionCode1
DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape GenerationCode2
Démélange, déconvolution et débruitage conjoints d'un modèle convolutif parcimonieux avec dérive instrumentale, par pénalisation de rapports de normes ou quasi-normes lissées (PENDANTSS)Code0
SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph GenerationCode1
ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram DigitizationCode1
Training Energy-Based Models with Diffusion Contrastive Divergences—0
Estimating Post-OCR Denoising Complexity on Numerical Texts—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