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 951–975 of 7282 papers

TitleStatusHype
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
Fréchet regression with implicit denoising and multicollinearity reduction—0
Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain GeneralizationCode0
Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms—0
Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence—0
ArchComplete: Autoregressive 3D Architectural Design Generation with Hierarchical Diffusion-Based UpsamplingCode0
Data-Driven Priors in the Maximum Entropy on the Mean Method for Linear Inverse ProblemsCode0
Bivariate Matrix-valued Linear Regression (BMLR): Finite-sample performance under Identifiability and Sparsity AssumptionsCode0
DiffusionAttacker: Diffusion-Driven Prompt Manipulation for LLM Jailbreak—0
Layer- and Timestep-Adaptive Differentiable Token Compression Ratios for Efficient Diffusion Transformers—0
A Conditional Diffusion Model for Electrical Impedance Tomography Image ReconstructionCode1
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
GANFusion: Feed-Forward Text-to-3D with Diffusion in GAN Space—0
Mixed geometry information regularization for image multiplicative denoising—0
FAP-CD: Fairness-Driven Age-Friendly Community Planning via Conditional Diffusion GenerationCode0
Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation—0
S^2DN: Learning to Denoise Unconvincing Knowledge for Inductive Knowledge Graph CompletionCode1
Multi-dimensional Visual Prompt Enhanced Image Restoration via Mamba-Transformer AggregationCode1
Guided Diffusion Model for Sensor Data Obfuscation—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