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 1526–1550 of 7282 papers

TitleStatusHype
Not All Steps are Created Equal: Selective Diffusion Distillation for Image ManipulationCode1
3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data InversionCode1
CERL: A Unified Optimization Framework for Light Enhancement with Realistic NoiseCode1
DiffPO: A causal diffusion model for learning distributions of potential outcomesCode1
Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic ManipulationCode1
Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEGCode1
Decoder Denoising Pretraining for Semantic SegmentationCode1
On Diffusion Modeling for Anomaly DetectionCode1
Differentiable Manifold Reconstruction for Point Cloud DenoisingCode1
CDLNet: Robust and Interpretable Denoising Through Deep Convolutional Dictionary LearningCode1
CDLNet: Noise-Adaptive Convolutional Dictionary Learning Network for Blind Denoising and DemosaicingCode1
Denoising Diffusion Autoencoders are Unified Self-supervised LearnersCode1
ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion TransformerCode1
DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion ModelsCode1
DiffCharge: Generating EV Charging Scenarios via a Denoising Diffusion ModelCode1
On the Benefit of Dual-domain Denoising in a Self-supervised Low-dose CT SettingCode1
On the Design of Deep Priors for Unsupervised Audio RestorationCode1
CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme ConditionsCode1
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic ModelsCode1
CCDM: Continuous Conditional Diffusion Models for Image GenerationCode1
Decoupled Data Consistency with Diffusion Purification for Image RestorationCode1
Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image AttenuationCode1
Open-set Adversarial DefenseCode1
DiffDA: a Diffusion Model for Weather-scale Data AssimilationCode1
CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion ModelCode1
Show:102550
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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