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 2401–2425 of 7282 papers

TitleStatusHype
Image Segmentation by Iterative Inference from Conditional Score EstimationCode0
Improving Chinese Story Generation via Awareness of Syntactic Dependencies and SemanticsCode0
Image Restoration Using Deep Regulated Convolutional NetworksCode0
Image Restoration using Plug-and-Play CNN MAP DenoisersCode0
CONCORD: Concept-Informed Diffusion for Dataset DistillationCode0
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip ConnectionsCode0
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip ConnectionsCode0
Concept Replacer: Replacing Sensitive Concepts in Diffusion Models via Precision LocalizationCode0
Image Reconstruction with Predictive Filter FlowCode0
Image quality measurements and denoising using Fourier Ring CorrelationsCode0
Advancing Medical Image Segmentation: Morphology-Driven Learning with Diffusion TransformerCode0
Image Embedding for Denoising Generative ModelsCode0
Image Denoising with Graph-Convolutional Neural NetworksCode0
Image Fusion via Sparse Regularization with Non-Convex PenaltiesCode0
Advancing low-field MRI with a universal denoising imaging transformer: Towards fast and high-quality imagingCode0
Image Denoising with Control over Deep Network HallucinationCode0
Image Inpainting via Tractable Steering of Diffusion ModelsCode0
Image denoising using complex-valued deep CNNCode0
Image denoising using deep CNN with batch renormalizationCode0
Accelerated Gradient Methods for Sparse Statistical Learning with Nonconvex PenaltiesCode0
IIDM: Image-to-Image Diffusion Model for Semantic Image SynthesisCode0
IFH: a Diffusion Framework for Flexible Design of Graph Generative ModelsCode0
Approximate Bayesian Computation with the Sliced-Wasserstein DistanceCode0
IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRICode0
Identifying Recurring Patterns with Deep Neural Networks for Natural Image DenoisingCode0
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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