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 3626–3650 of 7282 papers

TitleStatusHype
Learning to Efficiently Sample from Diffusion Probabilistic Models—0
Learning to Generate Genotypes with Neural Networks—0
Topology Guidance: Controlling the Outputs of Generative Models via Vector Field Topology—0
Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism—0
Learning to Navigate by Growing Deep Networks—0
Representation Learning for High-Dimensional Data Collection under Local Differential Privacy—0
Learning to Predict on Octree for Scalable Point Cloud Geometry Coding—0
Learning to Rank Broad and Narrow Queries in E-Commerce—0
Learning to Rank Intents in Voice Assistants—0
A Deep Learning Approach to Structured Signal Recovery—0
Learning to See Low-Light Images via Feature Domain Adaptation—0
Geometry-aware Two-scale PIFu Representation for Human Reconstruction—0
A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images—0
Predicting waves in fluids with deep neural network—0
Clean or Annotate: How to Spend a Limited Data Collection Budget—0
Learn to Optimize Denoising Scores for 3D Generation: A Unified and Improved Diffusion Prior on NeRF and 3D Gaussian Splatting—0
Learn to See Faster: Pushing the Limits of High-Speed Camera with Deep Underexposed Image Denoising—0
Wavelet-denoising on hardware devices with Perfect Reconstruction, low latency and adaptive thresholding—0
Diffusion-driven lensless fiber endomicroscopic quantitative phase imaging towards digital pathology—0
Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers—0
Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detection—0
Wavelet Integrated Convolutional Neural Network for ECG Signal Denoising—0
Total variation regularization for manifold-valued data—0
Leverage Unlabeled Data for Abstractive Speech Summarization with Self-Supervised Learning and Back-Summarization—0
Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification—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