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

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Showing 6401–6450 of 7282 papers

TitleStatusHype
Enhanced CNN for image denoising—0
Deep learning for denoisingCode0
Texture variation adaptive image denoising with nonlocal PCA—0
Convolutional Deblurring for Natural ImagingCode0
Patch-based Interferometric Phase Estimation via Mixture of Gaussian Density Modelling & Non-local Averaging in the Complex Domain—0
Multi-Domain Processing via Hybrid Denoising Networks for Speech Enhancement—0
Investigating the effect of residual and highway connections in speech enhancement modelsCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and EnhancementCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and EnhancementCode0
Heteroskedastic PCA: Algorithm, Optimality, and ApplicationsCode0
Learning Multi-Layer Transform Models—0
Unsupervised Neural Text SimplificationCode0
The Wasserstein transform—0
DN-ResNet: Efficient Deep Residual Network for Image Denoising—0
Deep Learning-Based Channel EstimationCode0
No-reference Image Denoising Quality Assessment—0
Cryo-CARE: Content-Aware Image Restoration for Cryo-Transmission Electron Microscopy DataCode0
Iterative Time-Varying Filter Algorithm Based on Discrete Linear Chirp Transform—0
Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes—0
Deep Learning for Image Denoising: A Survey—0
Seeing Beyond Appearance - Mapping Real Images into Geometrical Domains for Unsupervised CAD-based Recognition—0
Deep learning cardiac motion analysis for human survival predictionCode0
Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness—0
Deep Decoder: Concise Image Representations from Untrained Non-convolutional NetworksCode0
The LMU Munich Unsupervised Machine Translation Systems—0
Phrase-Based \& Neural Unsupervised Machine Translation—0
An Encoder-Decoder Approach to the Paradigm Cell Filling ProblemCode0
Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient NetworkCode0
Modelling local phase of images and textures with applications in phase denoising and phase retrieval—0
An End-to-End Deep Learning Architecture for Classification of Malware’s Binary Content—0
Image Reconstruction Using Deep Learning—0
Autonomous Deep Learning: Incremental Learning of Denoising Autoencoder for Evolving Data Streams—0
Low Frequency Adversarial PerturbationCode0
A Directed Graph Fourier Transform with Spread Frequency Components—0
Unsupervised parameter selection for denoising with the elastic net—0
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network—0
Enhanced 3DTV Regularization and Its Applications on Hyper-spectral Image Denoising and Compressed Sensing—0
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples—0
Label Denoising with Large Ensembles of Heterogeneous Neural Networks—0
Improved Techniques for Adversarial Discriminative Domain Adaptation—0
A Brief Review of Real-World Color Image DenoisingCode0
Unsupervised Sentence Compression using Denoising Auto-EncodersCode0
Online Adaptive Image Reconstruction (OnAIR) Using Dictionary Models—0
Connecting Image Denoising and High-Level Vision Tasks via Deep LearningCode0
Hierarchically Learned View-Invariant Representations for Cross-View Action Recognition—0
Real-time Single-channel Dereverberation and Separation with Time-domainAudio Separation Network—0
Deep Boosting for Image Denoising—0
Hierarchical Relational Networks for Group Activity Recognition and RetrievalCode0
DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNsCode0
Into the Twilight Zone: Depth Estimation using Joint Structure-Stereo Optimization—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