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

Showing 17761800 of 7282 papers

TitleStatusHype
Boundary-Denoising for Video Activity LocalizationCode0
BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented DialogCode0
Microscopy Image Restoration with Deep Wiener-Kolmogorov filtersCode0
MicroSSIM: Improved Structural Similarity for Comparing Microscopy DataCode0
MFM-DA: Instance-Aware Adaptor and Hierarchical Alignment for Efficient Domain Adaptation in Medical Foundation ModelsCode0
Lightweight network towards real-time image denoising on mobile devicesCode0
MFTF: Mask-free Training-free Object Level Layout Control Diffusion ModelCode0
Meta-DiffuB: A Contextualized Sequence-to-Sequence Text Diffusion Model with Meta-ExplorationCode0
Metaphor Detection with Effective Context DenoisingCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and EnhancementCode0
Mesh Denoising with Facet Graph ConvolutionsCode0
Multi-Agent Feedback Enabled Neural Networks for Intelligent CommunicationsCode0
An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challengesCode0
Boosting Black-box Attack to Deep Neural Networks with Conditional Diffusion ModelsCode0
Boosted Locality Sensitive Hashing: Discriminative Binary Codes for Source SeparationCode0
Medical image denoising using convolutional denoising autoencodersCode0
MCRAGE: Synthetic Healthcare Data for FairnessCode0
Generic Unsupervised Optimization for a Latent Variable Model With Exponential Family ObservablesCode0
A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant informationCode0
MaskPure: Improving Defense Against Text Adversaries with Stochastic PurificationCode0
Mask-GVAE: Blind Denoising Graphs via PartitionCode0
Block Coordinate Regularization by DenoisingCode0
MaskMedPaint: Masked Medical Image Inpainting with Diffusion Models for Mitigation of Spurious CorrelationsCode0
Maximum Likelihood Training for Score-Based Diffusion ODEs by High-Order Denoising Score MatchingCode0
Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning MethodsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified