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 1851–1900 of 7282 papers

TitleStatusHype
FORA: Fast-Forward Caching in Diffusion Transformer AccelerationCode2
An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations—0
Improving Diffusion Inverse Problem Solving with Decoupled Noise AnnealingCode2
ModWaveMLP: MLP-Based Mode Decomposition and Wavelet Denoising Model to Defeat Complex Structures in Traffic ForecastingCode1
Posterior Sampling with Denoising Oracles via Tilted Transport—0
Instruct-IPT: All-in-One Image Processing Transformer via Weight ModulationCode3
Consistency Purification: Effective and Efficient Diffusion Purification towards Certified Robustness—0
Learning Frequency-Aware Dynamic Transformers for All-In-One Image Restoration—0
Diffusion Models and Representation Learning: A SurveyCode2
Chest-Diffusion: A Light-Weight Text-to-Image Model for Report-to-CXR Generation—0
SVG: 3D Stereoscopic Video Generation via Denoising Frame Matrix—0
DPEC: Dual-Path Error Compensation Method for Enhanced Low-Light Image ClarityCode1
DiffuseDef: Improved Robustness to Adversarial Attacks via Iterative DenoisingCode0
DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems—0
Deep Unfolding-Aided Parameter Tuning for Plug-and-Play-Based Video Snapshot Compressive Imaging—0
ScoreFusion: Fusing Score-based Generative Models via Kullback-Leibler Barycenters—0
Comprehensive Generative Replay for Task-Incremental Segmentation with Concurrent Appearance and Semantic ForgettingCode0
Diminishing Stereotype Bias in Image Generation Model using Reinforcemenlent Learning Feedback—0
Denoising as Adaptation: Noise-Space Domain Adaptation for Image RestorationCode2
Generative artificial intelligence in ophthalmology: multimodal retinal images for the diagnosis of Alzheimer's disease with convolutional neural networks—0
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy TrainingCode0
Director3D: Real-world Camera Trajectory and 3D Scene Generation from TextCode3
GradCheck: Analyzing classifier guidance gradients for conditional diffusion sampling—0
FaceScore: Benchmarking and Enhancing Face Quality in Human GenerationCode2
Debiased Recommendation with Noisy FeedbackCode0
Stationary and Sparse Denoising Approach for Corticomuscular Causality Estimation—0
GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New InsightsCode2
ResMaster: Mastering High-Resolution Image Generation via Structural and Fine-Grained Guidance—0
UNICAD: A Unified Approach for Attack Detection, Noise Reduction and Novel Class Identification—0
On Instabilities of Unsupervised Denoising Diffusion Models in Magnetic Resonance Imaging Reconstruction—0
TimeAutoDiff: Combining Autoencoder and Diffusion model for time series tabular data synthesizingCode1
Pivotal Auto-Encoder via Self-Normalizing ReLU—0
Remaining useful life prediction of rolling bearings based on refined composite multi-scale attention entropy and dispersion entropy—0
Latent diffusion models for parameterization and data assimilation of facies-based geomodels—0
VividDreamer: Towards High-Fidelity and Efficient Text-to-3D Generation—0
ECLIPSE: Expunging Clean-label Indiscriminate Poisons via Sparse Diffusion PurificationCode0
Using Neural Networks for Data Cleaning in Weather Datasets—0
Diffusion-Based Failure Sampling for Evaluating Safety-Critical Autonomous SystemsCode0
Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models—0
Zero-Shot Image Denoising for High-Resolution Electron MicroscopyCode1
Surgical Triplet Recognition via Diffusion Model—0
RobGC: Towards Robust Graph Condensation—0
InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized RationalesCode2
Neural Residual Diffusion Models for Deep Scalable Vision GenerationCode0
MaskPure: Improving Defense Against Text Adversaries with Stochastic PurificationCode0
Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User FeedbackCode4
Training Diffusion Models with Federated Learning—0
Immiscible Diffusion: Accelerating Diffusion Training with Noise AssignmentCode2
Variational Distillation of Diffusion Policies into Mixture of Experts—0
Evaluating the design space of diffusion-based generative models—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