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 4676–4700 of 7282 papers

TitleStatusHype
PSyDUCK: Training-Free Steganography for Latent Diffusion—0
Zero-Shot Low-dose CT Denoising via Sinogram Flicking—0
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems—0
PTQ4ADM: Post-Training Quantization for Efficient Text Conditional Audio Diffusion Models—0
UnitY: Two-pass Direct Speech-to-speech Translation with Discrete Units—0
PUF-Phenotype: A Robust and Noise-Resilient Approach to Aid Intra-Group-based Authentication with DRAM-PUFs Using Machine Learning—0
Universal Architectures for the Learning of Polyhedral Norms and Convex Regularizers—0
Universal Denoising Networks : A Novel CNN Architecture for Image Denoising—0
PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptions—0
Pursuing Temporal-Consistent Video Virtual Try-On via Dynamic Pose Interaction—0
Pushing Joint Image Denoising and Classification to the Edge—0
Towards Real-World Video Denosing: A Practical Video Denosing Dataset and Network—0
Unknown sparsity in compressed sensing: Denoising and inference—0
Pyramidal Denoising Diffusion Probabilistic Models—0
Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation—0
MDT-A2G: Exploring Masked Diffusion Transformers for Co-Speech Gesture Generation—0
QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models—0
qMRI Diffuser: Quantitative T1 Mapping of the Brain using a Denoising Diffusion Probabilistic Model—0
QR code denoising using parallel Hopfield networks—0
QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping—0
Qua^2SeDiMo: Quantifiable Quantization Sensitivity of Diffusion Models—0
Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities—0
Quantifying Noise of Dynamic Vision Sensor—0
Quantitative and Qualitative Evaluation of NLM and Wavelet Methods in Image Enhancement—0
Quantifying Climate Change Impacts on Renewable Energy Generation: A Super-Resolution Recurrent Diffusion Model—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