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

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Showing 3301–3350 of 7282 papers

TitleStatusHype
Autoregressive Diffusion Model for Graph GenerationCode1
Not All Steps are Created Equal: Selective Diffusion Distillation for Image ManipulationCode1
Diffusion Models Beat GANs on Image ClassificationCode1
Noise-aware Speech Enhancement using Diffusion Probabilistic ModelCode1
A Novel Truncated Norm Regularization Method for Multi-channel Color Image DenoisingCode0
ExposureDiffusion: Learning to Expose for Low-light Image EnhancementCode1
Multitemporal SAR images change detection and visualization using RABASAR and simplified GLR—0
Certified Robustness for Large Language Models with Self-DenoisingCode1
Image Denoising and the Generative Accumulation of PhotonsCode1
Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0—0
Explainable Artificial Intelligence driven mask design for self-supervised seismic denoising—0
Quantum Image Denoising: A Framework via Boltzmann Machines, QUBO, and Quantum Annealing—0
Denoising Simulated Low-Field MRI (70mT) using Denoising Autoencoders (DAE) and Cycle-Consistent Generative Adversarial Networks (Cycle-GAN)—0
Exposing the Fake: Effective Diffusion-Generated Images Detection—0
On the Importance of Denoising when Learning to Compress ImagesCode0
Physics-informed Machine Learning for Calibrating Macroscopic Traffic Flow Models—0
Geometric Neural Diffusion ProcessesCode1
Bio-Inspired Night Image Enhancement Based on Contrast Enhancement and Denoising—0
Image Reconstruction using Enhanced Vision Transformer—0
DDGM: Solving inverse problems by Diffusive Denoising of Gradient-based Minimization—0
Metropolis Sampling for Constrained Diffusion Models—0
On the Vulnerability of DeepFake Detectors to Attacks Generated by Denoising Diffusion Models—0
Timbre transfer using image-to-image denoising diffusion implicit models—0
Geometric Constraints in Probabilistic Manifolds: A Bridge from Molecular Dynamics to Structured Diffusion Processes—0
Learning Spatial Features from Audio-Visual Correspondence in Egocentric Videos—0
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