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 6601–6625 of 7282 papers

TitleStatusHype
Nonlocality-Reinforced Convolutional Neural Networks for Image DenoisingCode0
Enhancement of land-use change modeling using convolutional neural networks and convolutional denoising autoencoders—0
prDeep: Robust Phase Retrieval with a Flexible Deep NetworkCode0
Poisson Image Denoising Using Best Linear Prediction: A Post-processing Framework—0
Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse CodingCode0
Learning Discriminative Multilevel Structured Dictionaries for Supervised Image Classification—0
Structured Uncertainty Prediction NetworksCode0
Deep BCD-Net Using Identical Encoding-Decoding CNN Structures for Iterative Image Recovery—0
Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative RefinementCode0
Divide, Denoise, and Defend against Adversarial Attacks—0
Fast, Trainable, Multiscale Denoising—0
3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network—0
Joint Demosaicing and Denoising with Perceptual Optimization on a Generative Adversarial Network—0
Recovering Loss to Followup Information Using Denoising Autoencoders—0
Temporal and volumetric denoising via quantile sparse image prior—0
client2vec: Towards Systematic Baselines for Banking Applications—0
Learning Deep Convolutional Networks for Demosaicing—0
Slice Sampling Particle Belief Propagation—0
Relational Autoencoder for Feature ExtractionCode0
Image denoising with generalized Gaussian mixture model patch priors—0
Weakly-supervised Dictionary Learning—0
Analysis of Fast Alternating Minimization for Structured Dictionary Learning—0
Denoising Arterial Spin Labeling Cerebral Blood Flow Images Using Deep Learning—0
Denoising Signals in Cognitive Radio Systems Using An Evolutionary Algorithm Based Adaptive Filter—0
Denoising Prior Driven Deep Neural Network for Image RestorationCode0
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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