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 2251–2300 of 7282 papers

TitleStatusHype
A locally statistical active contour model for SAR image segmentation can be solved by denoising algorithms—0
Deep Sparse Coding Using Optimized Linear Expansion of Thresholds—0
Bayesian ECG reconstruction using denoising diffusion generative models—0
Adaptive Feature Discrimination and Denoising for Asymmetric Text Matching—0
DeepSpace: Dynamic Spatial and Source Cue Based Source Separation for Dialog Enhancement—0
Bayesian Cramér-Rao Bound Estimation with Score-Based Models—0
Almost Unsupervised Text to Speech and Automatic Speech Recognition—0
Accelerated graph-based spectral polynomial filters—0
Empirical Bayesian image restoration by Langevin sampling with a denoising diffusion implicit prior—0
Empirical robustification of pre-trained classifiers—0
Encoding in the Dark Grand Challenge: An Overview—0
Bayesian Conditional GAN for MRI Brain Image Synthesis—0
Emergency Communication: OTFS-Based Semantic Transmission with Diffusion Noise Suppression—0
Batch-less stochastic gradient descent for compressive learning of deep regularization for image denoising—0
Deep Reward Supervisions for Tuning Text-to-Image Diffusion Models—0
Multi-weather Cross-view Geo-localization Using Denoising Diffusion Models—0
Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography—0
Adapting Language Models for Non-Parallel Author-Stylized Rewriting—0
Basis Pursuit Denoise with Nonsmooth Constraints—0
Adaptive Extensions of Unbiased Risk Estimators for Unsupervised Magnetic Resonance Image Denoising—0
EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluation—0
EmoDiff: Intensity Controllable Emotional Text-to-Speech with Soft-Label Guidance—0
Basis Prediction Networks for Effective Burst Denoising with Large Kernels—0
Adaptive Estimation of Multivariate Piecewise Polynomials and Bounded Variation Functions by Optimal Decision Trees—0
Accelerated graph-based nonlinear denoising filters—0
Deep priors for satellite image restoration with accurate uncertainties—0
Deep Point Set Resampling via Gradient Fields—0
Deep Point Cloud Reconstruction—0
Deep Plug-and-play Prior for Low-rank Tensor Completion—0
BandRC: Band Shifted Raised Cosine Activated Implicit Neural Representations—0
All-Optical Nonlinear Diffractive Deep Network for Ultrafast Image Denoising—0
EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks—0
EMoG: Synthesizing Emotive Co-speech 3D Gesture with Diffusion Model—0
Deep Photon Mapping—0
Adaptive Estimation and Learning under Temporal Distribution Shift—0
All-optical image denoising using a diffractive visual processor—0
Embedding models through the lens of Stable Coloring—0
EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding—0
DeepPCR: Parallelizing Sequential Operations in Neural Networks—0
Balancing the Style-Content Trade-Off in Sentiment Transfer UsingPolarity-Aware Denoising—0
All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation—0
Emage: Non-Autoregressive Text-to-Image Generation—0
E-MD3C: Taming Masked Diffusion Transformers for Efficient Zero-Shot Object Customization—0
Deep Nonparametric Estimation of Intrinsic Data Structures by Chart Autoencoders: Generalization Error and Robustness—0
Deep Nonparametric Convexified Filtering for Computational Photography, Image Synthesis and Adversarial Defense—0
Balancing Act: Distribution-Guided Debiasing in Diffusion Models—0
Deep Noise Suppression With Non-Intrusive PESQNet Supervision Enabling the Use of Real Training Data—0
Deep Noise Suppression Maximizing Non-Differentiable PESQ Mediated by a Non-Intrusive PESQNet—0
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE—0
Deep Neural Networks to Recover Unknown Physical Parameters from Oscillating Time Series—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