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 2576–2600 of 7282 papers

TitleStatusHype
Generative AI Meets Semantic Communication: Evolution and Revolution of Communication Tasks—0
Multi-Dimensional Framework for EEG Signal Processing and Denoising Through Tensor-based Architecture—0
Can Active Label Correction Improve LLM-based Modular AI Systems?—0
Score Distillation Sampling with Learned Manifold Corrective—0
Diffusion-based Pose Refinement and Muti-hypothesis Generation for 3D Human Pose EstimaitonCode1
Representative Feature Extraction During Diffusion Process for Sketch Extraction with One Example—0
EDA-DM: Enhanced Distribution Alignment for Post-Training Quantization of Diffusion ModelsCode1
Dynamic Addition of Noise in a Diffusion Model for Anomaly DetectionCode1
Attack-Resilient Image Watermarking Using Stable DiffusionCode2
Design a Metric Robust to Complicated High Dimensional Noise for Efficient Manifold Denoising—0
ROIC-DM: Robust Text Inference and Classification via Diffusion Model—0
Physics-informed Neural Networks for Encoding Dynamics in Real Physical Systems—0
SAR Despeckling via Regional Denoising Diffusion Probabilistic Model—0
MirrorDiffusion: Stabilizing Diffusion Process in Zero-shot Image Translation by Prompts Redescription and Beyond—0
Tailoring Frictional Properties of Surfaces Using Diffusion Models—0
Geometric-Facilitated Denoising Diffusion Model for 3D Molecule GenerationCode0
Denoising Vision TransformersCode3
Two-stage Progressive Residual Dense Attention Network for Image DenoisingCode0
Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational PosteriorsCode0
Bring Metric Functions into Diffusion Models—0
Improving Diffusion-Based Image Synthesis with Context Prediction—0
DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge DetectionCode3
Boosting of Implicit Neural Representation-based Image DenoiserCode1
DDPM based X-ray Image Synthesizer—0
DiffYOLO: Object Detection for Anti-Noise via YOLO and Diffusion 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