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 3226–3250 of 7282 papers

TitleStatusHype
Comprehensive Generative Replay for Task-Incremental Segmentation with Concurrent Appearance and Semantic ForgettingCode0
DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems—0
DiffuseDef: Improved Robustness to Adversarial Attacks via Iterative DenoisingCode0
Diminishing Stereotype Bias in Image Generation Model using Reinforcemenlent Learning Feedback—0
Generative artificial intelligence in ophthalmology: multimodal retinal images for the diagnosis of Alzheimer's disease with convolutional neural networks—0
GradCheck: Analyzing classifier guidance gradients for conditional diffusion sampling—0
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy TrainingCode0
ResMaster: Mastering High-Resolution Image Generation via Structural and Fine-Grained Guidance—0
Stationary and Sparse Denoising Approach for Corticomuscular Causality Estimation—0
UNICAD: A Unified Approach for Attack Detection, Noise Reduction and Novel Class Identification—0
Debiased Recommendation with Noisy FeedbackCode0
Pivotal Auto-Encoder via Self-Normalizing ReLU—0
On Instabilities of Unsupervised Denoising Diffusion Models in Magnetic Resonance Imaging Reconstruction—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
Using Neural Networks for Data Cleaning in Weather Datasets—0
ECLIPSE: Expunging Clean-label Indiscriminate Poisons via Sparse Diffusion PurificationCode0
VividDreamer: Towards High-Fidelity and Efficient Text-to-3D Generation—0
Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models—0
Diffusion-Based Failure Sampling for Evaluating Safety-Critical Autonomous SystemsCode0
Neural Residual Diffusion Models for Deep Scalable Vision GenerationCode0
RobGC: Towards Robust Graph Condensation—0
Surgical Triplet Recognition via Diffusion Model—0
Restorer: Removing Multi-Degradation with All-Axis Attention and Prompt GuidanceCode0
MaskPure: Improving Defense Against Text Adversaries with Stochastic PurificationCode0
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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