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 6476–6500 of 7282 papers

TitleStatusHype
User Loss -- A Forced-Choice-Inspired Approach to Train Neural Networks directly by User Interaction—0
Multi-View Networks for Denoising of Arbitrary Numbers of Channels—0
Decouple Learning for Parameterized Image OperatorsCode0
Convolutional Neural Networks Analyzed via Inverse Problem Theory and Sparse Representations—0
Fully Convolutional Pixel Adaptive Image DenoiserCode1
The Deep Kernelized Autoencoder—0
Method for motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MRI of the liver—0
Learning Generic Diffusion Processes for Image Restoration—0
Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging dataCode0
Iterative Joint Image Demosaicking and Denoising using a Residual Denoising NetworkCode0
Combining a Context Aware Neural Network with a Denoising Autoencoder for Measuring String SimilaritiesCode0
Global Optimality in Separable Dictionary Learning with Applications to the Analysis of Diffusion MRI—0
A salt and pepper noise image denoising method based on the generative classification—0
Toward Convolutional Blind Denoising of Real PhotographsCode1
Deep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations—0
A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising—0
Microarrays denoising via smoothing of coefficients in wavelet domain—0
Image Restoration Using Conditional Random Fields and Scale Mixtures of Gaussians—0
From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image RestorationCode1
Sparse Representation and Non-Negative Matrix Factorization for image denoise—0
Denoising Auto-encoder with Recurrent Skip Connections and Residual Regression for Music Source Separation—0
Learning Personalized Representation for Inverse Problems in Medical Imaging Using Deep Neural Network—0
Patient representation learning and interpretable evaluation using clinical notes—0
Sparse Geometric Representation Through Local Shape ProbingCode0
Connecting Supervised and Unsupervised Sentence Embeddings—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