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
Your ViT is Secretly a Hybrid Discriminative-Generative Diffusion ModelCode1
Self-supervised training of deep denoisers in multi-coil MRI considering noise correlations—0
On a Mechanism Framework of Autoencoders—0
Learning Semantic Correspondence with Sparse AnnotationsCode1
Uni6Dv2: Noise Elimination for 6D Pose Estimation—0
Recent Progress in Transformer-based Medical Image Analysis—0
Multilayer Fisher extreme learning machine for classification—0
High-Frequency Space Diffusion Models for Accelerated MRICode1
Convergence of denoising diffusion models under the manifold hypothesis—0
A data-driven modular architecture with denoising autoencoders for health indicator construction in a manufacturing process—0
Learning Degradation Representations for Image DeblurringCode1
A Topological Loss Function: Image Denoising on a Low-Light Dataset—0
Denoising Induction Motor Sounds Using an Autoencoder—0
FRA-RIR: Fast Random Approximation of the Image-source MethodCode2
SciAnnotate: A Tool for Integrating Weak Labeling Sources for Sequence LabelingCode0
Image denoising in acoustic field microscopy—0
Fixed-Point Automatic Differentiation of Forward--Backward Splitting Algorithms for Partly Smooth Functions—0
Rethinking Degradation: Radiograph Super-Resolution via AID-SRGANCode1
An intertwined neural network model for EEG classification in brain-computer interfaces—0
Unsupervised Graph Spectral Feature Denoising for Crop Yield Prediction—0
Pyramidal Denoising Diffusion Probabilistic Models—0
Exploring Generative Neural Temporal Point ProcessCode1
Decay2Distill: Leveraging spatial perturbation and regularization for self-supervised image denoising—0
Multimodal sensor fusion in the latent representation space—0
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq ModelCode2
Show:102550
← PrevPage 175 of 292Next →
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