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 2501–2550 of 7282 papers

TitleStatusHype
Random Conditioning for Diffusion Model Compression with Distillation—0
A Study on Context Length and Efficient Transformers for Biomedical Image Analysis—0
Token Pruning for Caching Better: 9 Times Acceleration on Stable Diffusion for FreeCode0
S-Diff: An Anisotropic Diffusion Model for Collaborative Filtering in Spectral Domain—0
Quantum Diffusion Model for Quark and Gluon Jet GenerationCode0
"Generative Models for Financial Time Series Data: Enhancing Signal-to-Noise Ratio and Addressing Data Scarcity in A-Share Market—0
Single-image reflection removal via self-supervised diffusion models—0
MAKIMA: Tuning-free Multi-Attribute Open-domain Video Editing via Mask-Guided Attention Modulation—0
Enhancing Diffusion Models for Inverse Problems with Covariance-Aware Posterior Sampling—0
Reinforced Label Denoising for Weakly-Supervised Audio-Visual Video Parsing—0
RAIN: Real-time Animation of Infinite Video Stream—0
Improving Generative Pre-Training: An In-depth Study of Masked Image Modeling and Denoising Models—0
Imperceptible Adversarial Attacks on Point Clouds Guided by Point-to-Surface Field—0
Discrete vs. Continuous Trade-offs for Generative Models—0
Conditional Balance: Improving Multi-Conditioning Trade-Offs in Image Generation—0
Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms—0
Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain GeneralizationCode0
Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence—0
Fréchet regression with implicit denoising and multicollinearity reduction—0
Bivariate Matrix-valued Linear Regression (BMLR): Finite-sample performance under Identifiability and Sparsity AssumptionsCode0
Data-Driven Priors in the Maximum Entropy on the Mean Method for Linear Inverse ProblemsCode0
ArchComplete: Autoregressive 3D Architectural Design Generation with Hierarchical Diffusion-Based UpsamplingCode0
DiffusionAttacker: Diffusion-Driven Prompt Manipulation for LLM Jailbreak—0
Layer- and Timestep-Adaptive Differentiable Token Compression Ratios for Efficient Diffusion Transformers—0
FAP-CD: Fairness-Driven Age-Friendly Community Planning via Conditional Diffusion GenerationCode0
Mixed geometry information regularization for image multiplicative denoising—0
GANFusion: Feed-Forward Text-to-3D with Diffusion in GAN Space—0
Complementary Advantages: Exploiting Cross-Field Frequency Correlation for NIR-Assisted Image Denoising—0
Positive2Negative: Breaking the Information-Lossy Barrier in Self-Supervised Single Image Denoising—0
Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation—0
Bright-NeRF:Brightening Neural Radiance Field with Color Restoration from Low-light Raw Images—0
Blind Deconvolution of Graph Signals: Robustness to Graph Perturbations—0
Qua^2SeDiMo: Quantifiable Quantization Sensitivity of Diffusion Models—0
Consistent Human Image and Video Generation with Spatially Conditioned DiffusionCode0
Enhancing Diffusion Models for High-Quality Image Generation—0
Guided Diffusion Model for Sensor Data Obfuscation—0
DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation—0
DiffusionTrend: A Minimalist Approach to Virtual Fashion Try-On—0
Denoising Nearest Neighbor Graph via Continuous CRF for Visual Re-ranking without Fine-tuning—0
Federated Unlearning Model Recovery in Data with Skewed Label Distributions—0
MMO-IG: Multi-Class and Multi-Scale Object Image Generation for Remote Sensing—0
Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion—0
Personalized Generative Low-light Image Denoising and Enhancement—0
E-CAR: Efficient Continuous Autoregressive Image Generation via Multistage Modeling—0
VIIS: Visible and Infrared Information Synthesis for Severe Low-light Image EnhancementCode0
C2F-TP: A Coarse-to-Fine Denoising Framework for Uncertainty-Aware Trajectory PredictionCode0
Guided and Variance-Corrected Fusion with One-shot Style Alignment for Large-Content Image GenerationCode0
Addressing Small and Imbalanced Medical Image Datasets Using Generative Models: A Comparative Study of DDPM and PGGANs with Random and Greedy K SamplingCode0
LazyDiT: Lazy Learning for the Acceleration of Diffusion Transformers—0
Consistent Diffusion: Denoising Diffusion Model with Data-Consistent Training for Image Restoration—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