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 3151–3200 of 7282 papers

TitleStatusHype
M2D2M: Multi-Motion Generation from Text with Discrete Diffusion Models—0
BRSR-OpGAN: Blind Radar Signal Restoration using Operational Generative Adversarial NetworkCode0
Underwater Acoustic Signal Denoising Algorithms: A Survey of the State-of-the-art—0
HPPP: Halpern-type Preconditioned Proximal Point Algorithms and Applications to Image RestorationCode0
Predictive Low Rank Matrix Learning under Partial Observations: Mixed-Projection ADMMCode0
FocusDiffuser: Perceiving Local Disparities for Camouflaged Object Detection—0
Enhanced Denoising of Optical Coherence Tomography Images Using Residual U-Net—0
EnergyDiff: Universal Time-Series Energy Data Generation using Diffusion ModelsCode0
DiffuX2CT: Diffusion Learning to Reconstruct CT Images from Biplanar X-Rays—0
GeoGuide: Geometric guidance of diffusion modelsCode0
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited DataCode0
Beta Sampling is All You Need: Efficient Image Generation Strategy for Diffusion Models using Stepwise Spectral Analysis—0
Magnetogram-to-Magnetogram: Generative Forecasting of Solar EvolutionCode0
Novel Hybrid Integrated Pix2Pix and WGAN Model with Gradient Penalty for Binary Images DenoisingCode0
Contrastive Sequential-Diffusion Learning: Non-linear and Multi-Scene Instructional Video SynthesisCode0
How Control Information Influences Multilingual Text Image Generation and Editing?Code0
Integrating Amortized Inference with Diffusion Models for Learning Clean Distribution from Corrupted Images—0
Temporal Residual Guided Diffusion Framework for Event-Driven Video Reconstruction—0
Backdoor Attacks against Image-to-Image Networks—0
Physics-Inspired Generative Models in Medical Imaging: A Review—0
Optical Diffusion Models for Image Generation—0
2D Neural Fields with Learned Discontinuities—0
Pre-training with Fractional Denoising to Enhance Molecular Property Prediction—0
Unsupervised Anomaly Detection Using Diffusion Trend Analysis—0
Fast and Robust Phase Retrieval via Deep Expectation-Consistent ApproximationCode0
Enhancing Few-Shot Stock Trend Prediction with Large Language Models—0
ECG Signal Denoising Using Multi-scale Patch Embedding and Transformers—0
DG-PIC: Domain Generalized Point-In-Context Learning for Point Cloud Understanding—0
Live2Diff: Live Stream Translation via Uni-directional Attention in Video Diffusion Models—0
ERD: Exponential Retinex decomposition based on weak space and hybrid nonconvex regularization and its denoising application—0
Haar Nuclear Norms with Applications to Remote Sensing Imagery Restoration—0
Systematic Evaluation of Neural Retrieval Models on the Touché 2020 Argument Retrieval Subset of BEIRCode0
What's the score? Automated Denoising Score Matching for Nonlinear Diffusions—0
DuInNet: Dual-Modality Feature Interaction for Point Cloud Completion—0
Accelerating Mobile Edge Generation (MEG) by Constrained Learning—0
Using Galaxy Evolution as Source of Physics-Based Ground Truth for Generative ModelsCode0
Implicit Regression in Subspace for High-Sensitivity CEST Imaging—0
LaSe-E2V: Towards Language-guided Semantic-Aware Event-to-Video Reconstruction—0
CrowdMoGen: Zero-Shot Text-Driven Collective Motion Generation—0
Enhancing super-resolution ultrasound localisation through multi-frame deconvolution exploiting spatiotemporal coherence—0
Diffusion Models: Tutorial and Survey—0
Structured Generations: Using Hierarchical Clusters to guide Diffusion ModelsCode0
Image-Conditional Diffusion Transformer for Underwater Image Enhancement—0
Mjolnir: Breaking the Shield of Perturbation-Protected Gradients via Adaptive Diffusion—0
Enhancing Label-efficient Medical Image Segmentation with Text-guided Diffusion Models—0
Linear Attention Based Deep Nonlocal Means Filtering for Multiplicative Noise Removal—0
A Domain Adaptation Model for Carotid Ultrasound: Image Harmonization, Noise Reduction, and Impact on Cardiovascular Risk Markers—0
Optimizing the image correction pipeline for pedestrian detection in the thermal-infrared domain—0
GSD: View-Guided Gaussian Splatting Diffusion for 3D Reconstruction—0
PROUD: PaRetO-gUided Diffusion Model for Multi-objective GenerationCode0
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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