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

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Showing 1901–1950 of 7282 papers

TitleStatusHype
Denoising random forests—0
Adaptive Whole-Body PET Image Denoising Using 3D Diffusion Models with ControlNet—0
Denoising instrumented mouthguard measurements of head impact kinematics with a convolutional neural network—0
Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation—0
Blockwise SURE Shrinkage for Non-Local Means—0
Denoising Score Matching with Random Fourier Features—0
Denoising Induction Motor Sounds Using an Autoencoder—0
Denoising Self-attentive Sequential Recommendation—0
Blind Denoising Autoencoder—0
Denoising Simulated Low-Field MRI (70mT) using Denoising Autoencoders (DAE) and Cycle-Consistent Generative Adversarial Networks (Cycle-GAN)—0
Blind Deconvolution of Graph Signals: Robustness to Graph Perturbations—0
Analysis and Synthesis Denoisers for Forward-Backward Plug-and-Play Algorithms—0
An Analysis on Quantizing Diffusion Transformers—0
Denoising Task Difficulty-based Curriculum for Training Diffusion Models—0
Analysis and Extension of Noisy-target Training for Unsupervised Target Signal Enhancement—0
Denoising Text to Speech with Frame-Level Noise Modeling—0
Denoising Time Cycle Modeling for Recommendation—0
Denoising Time Series Data Using Asymmetric Generative Adversarial Networks—0
Denoising Improves Latent Space Geometry in Text Autoencoders—0
Denoising VAE as an Explainable Feature Reduction and Diagnostic Pipeline for Autism Based on Resting state fMRI—0
Boosting Fast and High-Quality Speech Synthesis with Linear Diffusion—0
Denoising via Repainting: an image denoising method using layer wise medical image repainting—0
Blind CT Image Quality Assessment Using DDPM-derived Content and Transformer-based Evaluator—0
Denoising Weak Lensing Mass Maps with Deep Learning—0
Denoising with a Joint-Embedding Predictive Architecture—0
Denoising without access to clean data using a partitioned autoencoder—0
Denoising Imaging Polarimetry by an Adapted BM3D Method—0
Denoising Hyperspectral Image with Non-i.i.d. Noise Structure—0
DenoMAE2.0: Improving Denoising Masked Autoencoders by Classifying Local Patches—0
Boosting the Performance of Plug-and-Play Priors via Denoiser Scaling—0
Blind Channel Estimation for Massive MIMO: A Deep Learning Assisted Approach—0
Dif-Fusion: Towards High Color Fidelity in Infrared and Visible Image Fusion with Diffusion Models—0
Denosing Using Wavelets and Projections onto the L1-Ball—0
DenseFormer: Learning Dense Depth Map from Sparse Depth and Image via Conditional Diffusion Model—0
DensePure: Understanding Diffusion Models towards Adversarial Robustness—0
Dense residual Transformer for image denoising—0
Dense-Sparse Deep Convolutional Neural Networks Training for Image Denoising—0
Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement—0
DiffuVST: Narrating Fictional Scenes with Global-History-Guided Denoising Models—0
Density-based Denoising of Point Cloud—0
Depth-guided NeRF Training via Earth Mover's Distance—0
Depth image denoising using nuclear norm and learning graph model—0
Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning—0
Blind Biological Sequence Denoising with Self-Supervised Set Learning—0
Denoising Hamiltonian Network for Physical Reasoning—0
Denoising guarantees for optimized sampling schemes in compressed sensing—0
Blind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation—0
DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images—0
Design a Metric Robust to Complicated High Dimensional Noise for Efficient Manifold Denoising—0
Analysing Diffusion Segmentation for Medical Images—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