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 3801–3825 of 7282 papers

TitleStatusHype
Towards Detailed Text-to-Motion Synthesis via Basic-to-Advanced Hierarchical Diffusion Model—0
Masked Autoencoders for Low dose CT denoising—0
ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images—0
Towards Efficient and Accurate Approximation: Tensor Decomposition Based on Randomized Block Krylov Iteration—0
Enhanced Controllability of Diffusion Models via Feature Disentanglement and Realism-Enhanced Sampling Methods—0
Towards Explainable Fusion and Balanced Learning in Multimodal Sentiment Analysis—0
AdarGCN: Adaptive Aggregation GCN for Few-Shot Learning—0
Masked Modeling Duo for Speech: Specializing General-Purpose Audio Representation to Speech using Denoising Distillation—0
Masked Modeling Duo: Towards a Universal Audio Pre-training Framework—0
Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models—0
Masking schemes for universal marginalisers—0
Towards Flexible, Scalable, and Adaptive Multi-Modal Conditioned Face Synthesis—0
MAS: Multi-view Ancestral Sampling for 3D motion generation using 2D diffusion—0
WD-DETR: Wavelet Denoising-Enhanced Real-Time Object Detection Transformer for Robot Perception with Event Cameras—0
Matrix denoising for weighted loss functions and heterogeneous signals—0
Matrix Denoising with Doubly Heteroscedastic Noise: Fundamental Limits and Optimal Spectral Methods—0
Matrix Product Operator Restricted Boltzmann Machines—0
Adaptive Whole-Body PET Image Denoising Using 3D Diffusion Models with ControlNet—0
Maximum a Posteriori on a Submanifold: a General Image Restoration Method with GAN—0
Maximum a posteriori signal recovery for optical coherence tomography angiography image generation and denoising—0
The Maximum Entropy on the Mean Method for Image Deblurring—0
May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations—0
MBD: Multi b-value Denoising of Diffusion Magnetic Resonance Images—0
McCaD: Multi-Contrast MRI Conditioned, Adaptive Adversarial Diffusion Model for High-Fidelity MRI Synthesis—0
MDAA-Diff: CT-Guided Multi-Dose Adaptive Attention Diffusion Model for PET Denoising—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