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 6551–6575 of 7282 papers

TitleStatusHype
Plug-and-Play AMC: Context Is King in Training-Free, Open-Set Modulation with LLMsCode0
Self-Supervised Image Prior Learning With GMM From a Single Noisy ImageCode0
Generative Simulations of The Solar Corona Evolution With Denoising Diffusion : Proof of ConceptCode0
Plug-and-play ISTA converges with kernel denoisersCode0
Restorer: Removing Multi-Degradation with All-Axis Attention and Prompt GuidanceCode0
Plug-and-Play Linear Attention for Pre-trained Image and Video Restoration ModelsCode0
Blind microscopy image denoising with a deep residual and multiscale encoder/decoder networkCode0
Plug-and-Play Methods Provably Converge with Properly Trained DenoisersCode0
Blind Restoration of Real-World Audio by 1D Operational GANsCode0
Negative Sampling with Adaptive Denoising Mixup for Knowledge Graph EmbeddingCode0
Negligible effect of brain MRI data preprocessing for tumor segmentationCode0
GenPlan: Generative Sequence Models as Adaptive PlannersCode0
Blind Universal Bayesian Image Denoising with Gaussian Noise Level LearningCode0
Diffusion Counterfactuals for Image RegressorsCode0
Learning Generalizable 3D Manipulation With 10 DemonstrationsCode0
Restoring Real-World Degraded Events Improves Deblurring QualityCode0
Adversarial Domain Adaptation for Cross-user Activity Recognition Using Diffusion-based Noise-centred LearningCode0
Diffusion Denoising Process for Perceptron Bias in Out-of-distribution DetectionCode0
White-Box Diffusion Transformer for single-cell RNA-seq generationCode0
Learning Generative Models using Denoising Density EstimatorsCode0
A survey of probabilistic generative frameworks for molecular simulationsCode0
GeoGuide: Geometric guidance of diffusion modelsCode0
Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamicsCode0
GeomCLIP: Contrastive Geometry-Text Pre-training for MoleculesCode0
Restoring Snow-Degraded Single Images With Wavelet in Vision TransformerCode0
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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