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 6276–6300 of 7282 papers

TitleStatusHype
Denoising Generalized Expectation-Consistent Approximation for MR Image RecoveryCode0
Denoising Graph Super-Resolution towards Improved Collider Event ReconstructionCode0
Reducing Spatial Fitting Error in Distillation of Denoising Diffusion ModelsCode0
A Fusion-Denoising Attack on InstaHide with Data AugmentationCode0
MTVHunter: Smart Contracts Vulnerability Detection Based on Multi-Teacher Knowledge TranslationCode0
Flashlight CNN Image DenoisingCode0
FlexControl: Computation-Aware ControlNet with Differentiable Router for Text-to-Image GenerationCode0
Single Image Denoising via a New Lightweight Learning-Based ModelCode0
A Robust Alternative for Graph Convolutional Neural Networks via Graph Neighborhood FiltersCode0
Cross-model Back-translated Distillation for Unsupervised Machine TranslationCode0
Multi-Agent Feedback Enabled Neural Networks for Intelligent CommunicationsCode0
Convolutional Deblurring for Natural ImagingCode0
Spend Wisely: Maximizing Post-Training Gains in Iterative Synthetic Data BoostrappingCode0
Denoising individual bias for a fairer binary submatrix detectionCode0
Deep Gaussian Denoiser Epistemic Uncertainty and Decoupled Dual-Attention FusionCode0
Refining Few-Step Text-to-Multiview Diffusion via Reinforcement LearningCode0
Beyond the Visible: Jointly Attending to Spectral and Spatial Dimensions with HSI-Diffusion for the FINCH SpacecraftCode0
Joint Enhancement and Denoising Method via Sequential DecompositionCode0
Convolutional Dictionary Learning: Acceleration and ConvergenceCode0
DeepASL: Kinetic Model Incorporated Loss for Denoising Arterial Spin Labeled MRI via Deep Residual LearningCode0
Patch-Craft Self-Supervised Training for Correlated Image DenoisingCode0
When A Conventional Filter Meets Deep Learning: Basis Composition Learning on Image FiltersCode0
Sparse learning of stochastic dynamic equationsCode0
Joint inference and input optimization in equilibrium networksCode0
Denoising modulo samples: k-NN regression and tightness of SDP relaxationCode0
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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