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 4901–4950 of 7282 papers

TitleStatusHype
A data-driven modular architecture with denoising autoencoders for health indicator construction in a manufacturing process—0
MAKIMA: Tuning-free Multi-Attribute Open-domain Video Editing via Mask-Guided Attention Modulation—0
Making Images from Images: Interleaving Denoising and Transformation—0
MalPhase: Fine-Grained Malware Detection Using Network Flow Data—0
Z-STAR+: A Zero-shot Style Transfer Method via Adjusting Style Distribution—0
A Data-Driven Gaussian Process Filter for Electrocardiogram Denoising—0
MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising—0
Mandarin Singing Voice Synthesis with Denoising Diffusion Probabilistic Wasserstein GAN—0
Towards Consistent and Controllable Image Synthesis for Face Editing—0
ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation—0
ManiDext: Hand-Object Manipulation Synthesis via Continuous Correspondence Embeddings and Residual-Guided Diffusion—0
Towards Controllable Diffusion Models via Reward-Guided Exploration—0
Manifold Learning Benefits GANs—0
Manifold Modeling in Embedded Space: A Perspective for Interpreting "Deep Image Prior"—0
Manifold Modeling in Quotient Space: Learning An Invariant Mapping with Decodability of Image Patches—0
Manifold Rewiring for Unlabeled Imaging—0
Map2Traj: Street Map Piloted Zero-shot Trajectory Generation with Diffusion Model—0
MAP-based Problem-Agnostic diffusion model for Inverse Problems—0
MAR-3D: Progressive Masked Auto-regressor for High-Resolution 3D Generation—0
MARBLE: Material Recomposition and Blending in CLIP-Space—0
A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems—0
Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion—0
Towards Deep Unsupervised SAR Despeckling with Blind-Spot Convolutional Neural Networks—0
Masked and Shuffled Blind Spot Denoising for Real-World Images—0
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
mDAE : modified Denoising AutoEncoder for missing data imputation—0
Show:102550
← PrevPage 99 of 146Next →
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