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 3601–3650 of 7282 papers

TitleStatusHype
A Variational Perspective on Solving Inverse Problems with Diffusion ModelsCode1
Dual Residual Attention Network for Image DenoisingCode1
Towards Prompt-robust Face Privacy Protection via Adversarial Decoupling Augmentation Framework—0
Diffusion-NAT: Self-Prompting Discrete Diffusion for Non-Autoregressive Text Generation—0
SST-ReversibleNet: Reversible-prior-based Spectral-Spatial Transformer for Efficient Hyperspectral Image ReconstructionCode1
DocDiff: Document Enhancement via Residual Diffusion ModelsCode2
Efficient and Degree-Guided Graph Generation via Discrete Diffusion ModelingCode1
Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising—0
2D medical image synthesis using transformer-based denoising diffusion probabilistic modelCode1
Steered Mixture-of-Experts Autoencoder Design for Real-Time Image Modelling and Denoising—0
Guided Image Synthesis via Initial Image Editing in Diffusion ModelCode0
Denoising-Contrastive Alignment for Continuous Sign Language Recognition—0
Contrastive Learning for Low-light Raw Denoising—0
A multimodal dynamical variational autoencoder for audiovisual speech representation learningCode0
DisenBooth: Identity-Preserving Disentangled Tuning for Subject-Driven Text-to-Image GenerationCode1
LayoutDM: Transformer-based Diffusion Model for Layout Generation—0
Multimodal-driven Talking Face Generation via a Unified Diffusion-based Generator—0
Denoising Multi-modal Sequential Recommenders with Contrastive Learning—0
Solving Inverse Problems with Score-Based Generative Priors learned from Noisy Data—0
Joint tone mapping and denoising of thermal infrared images via multi-scale Retinex and multi-task learning—0
RViDeformer: Efficient Raw Video Denoising Transformer with a Larger Benchmark Dataset—0
Sparsity-Aware Optimal Transport for Unsupervised Restoration Learning—0
Unified Noise-aware Network for Low-count PET Denoising—0
Cycle-guided Denoising Diffusion Probability Model for 3D Cross-modality MRI Synthesis—0
Knowledge-refined Denoising Network for Robust RecommendationCode1
Non-Contact Heart Rate Measurement from Deteriorated Videos—0
Deep sound-field denoiser: optically-measured sound-field denoising using deep neural networkCode0
Single-View Height Estimation with Conditional Diffusion Probabilistic Models—0
MRI Recovery with Self-Calibrated Denoisers without Fully-Sampled DataCode0
The Score-Difference Flow for Implicit Generative Modeling—0
NoiseTrans: Point Cloud Denoising with Transformers—0
DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic ModelCode1
Score-Based Diffusion Models as Principled Priors for Inverse ImagingCode1
The Devil is in the Upsampling: Architectural Decisions Made Simpler for Denoising with Deep Image PriorCode1
Conditional Denoising Diffusion for Sequential Recommendation—0
Fast Diffusion Probabilistic Model Sampling through the lens of Backward Error Analysis—0
Automatically identifying ordinary differential equations from dataCode1
Heart Rate Extraction from Abdominal Audio Signals—0
Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inferenceCode1
Cross-domain Denoising for Low-dose Multi-frame Spiral Computed TomographyCode1
H2TF for Hyperspectral Image Denoising: Where Hierarchical Nonlinear Transform Meets Hierarchical Matrix Factorization—0
An Attention Free Conditional Autoencoder For Anomaly Detection in Cryptocurrencies—0
Revisiting Implicit Neural Representations in Low-Level VisionCode1
Collaborative Diffusion for Multi-Modal Face Generation and EditingCode2
Denoising Cosine Similarity: A Theory-Driven Approach for Efficient Representation Learning—0
Self-supervised Image Denoising with Downsampled Invariance Loss and Conditional Blind-Spot Network—0
Multi-scale Adaptive Fusion Network for Hyperspectral Image DenoisingCode1
Denoising Diffusion Medical Models—0
Look ATME: The Discriminator Mean Entropy Needs AttentionCode1
A Comparison of Image Denoising MethodsCode1
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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