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 4351–4375 of 7282 papers

TitleStatusHype
Imagine360: Immersive 360 Video Generation from Perspective Anchor—0
Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation—0
Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR << 1 Imaging—0
Imaging with Kantorovich-Rubinstein discrepancy—0
A Domain Adaptation Model for Carotid Ultrasound: Image Harmonization, Noise Reduction, and Impact on Cardiovascular Risk Markers—0
Impact of Benign Modifications on Discriminative Performance of Deepfake Detectors—0
Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders—0
Impact of PCA-based preprocessing and different CNN structures on deformable registration of sonograms—0
Imperceptible Adversarial Attacks on Point Clouds Guided by Point-to-Surface Field—0
Predicting the Radiation Field of Molecular Clouds using Denoising Diffusion Probabilistic Models—0
Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions—0
ADMIRE: a locally adaptive single-image, non-uniformity correction and denoising algorithm: application to uncooled IR camera—0
Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI—0
Implicit neural representations for end-to-end PET reconstruction—0
Implicit Regression in Subspace for High-Sensitivity CEST Imaging—0
IM-Portrait: Learning 3D-aware Video Diffusion for Photorealistic Talking Heads from Monocular VideosC—0
Improved Adaptive Type-2 Fuzzy Filter with Exclusively Two Fuzzy Membership Function for Filtering Salt and Pepper Noise—0
Improved DDIM Sampling with Moment Matching Gaussian Mixtures—0
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control—0
Improved Detection of Adversarial Images Using Deep Neural Networks—0
Detection and Imputation based Two-Stage Denoising Diffusion Power System Measurement Recovery under Cyber-Physical Uncertainties—0
Improved far-field speech recognition using Joint Variational Autoencoder—0
Improved functional MRI activation mapping in white matter through diffusion-adapted spatial filtering—0
A Dive into SAM Prior in Image Restoration—0
Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting—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