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 5851–5900 of 7282 papers

TitleStatusHype
Deep Learning-based Modulation Detection for NOMA Systems—0
MIMO Speech Compression and Enhancement Based on Convolutional Denoising Autoencoder—0
Wavelet based multivariate signal denoising using Mahalanobis distance and EDF statistics—0
An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challengesCode0
Revisiting Role of Autoencoders in Adversarial Settings—0
Attention-based network for low-light image enhancement—0
One Size Fits All: Can We Train One Denoiser for All Noise Levels?—0
Inverse problems with second-order Total Generalized Variation constraints—0
Self-supervised Dynamic CT Perfusion Image Denoising with Deep Neural Networks—0
Learning Equations from Biological Data with Limited Time SamplesCode0
Various Total Variation for Snapshot Video Compressive Imaging—0
A Learning-from-noise Dilated Wide Activation Network for denoising Arterial Spin Labeling (ASL) Perfusion Images—0
Low-Dose CT Image Denoising Using Parallel-Clone Networks—0
Real-time and high-throughput Raman signal extraction and processing in CARS hyperspectral imaging—0
A Survey on Patch-based Synthesis: GPU Implementation and Optimization—0
Multi-Level Generative Models for Partial Label Learning with Non-random Label Noise—0
A Weighted Difference of Anisotropic and Isotropic Total Variation for Relaxed Mumford-Shah Color and Multiphase Image SegmentationCode0
A Showcase of the Use of Autoencoders in Feature Learning ApplicationsCode0
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and ResultsCode0
Encoding in the Dark Grand Challenge: An Overview—0
Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer—0
A Bayesian traction force microscopy method with automated denoising in a user-friendly software package—0
Robust Non-Linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering—0
Deep Neural Network-Based Quantized Signal Reconstruction for DOA Estimation—0
Deep Encoder-Decoder Neural Network for Fingerprint Image Denoising and Inpainting—0
Deeply Cascaded U-Net for Multi-Task Image Processing—0
Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders—0
Learning to Rank Intents in Voice Assistants—0
Adversarial Feature Learning and Unsupervised Clustering based Speech Synthesis for Found Data with Acoustic and Textual Noise—0
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
Identity Enhanced Residual Image DenoisingCode0
Attention Based Real Image RestorationCode0
Deep Photon Mapping—0
Kalman Filter and Wavelet Cross-correlation for VHF Broadband Interferometer Lightning Mapping—0
Accurate Graph Filtering in Wireless Sensor Networks—0
A Review of an Old Dilemma: Demosaicking First, or Denoising First?—0
Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix ApproximationCode0
SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution—0
Virtual SAR: A Synthetic Dataset for Deep Learning based Speckle Noise Reduction Algorithms—0
Frequency-Weighted Robust Tensor Principal Component Analysis—0
The Role of Redundant Bases and Shrinkage Functions in Image Denoising—0
Lottery Hypothesis based Unsupervised Pre-training for Model Compression in Federated Learning—0
CommUnet: U-net decoder for convolutional codes in communication—0
Superkernel Neural Architecture Search for Image Denoising—0
Deep Learning Improves Contrast in Low-Fluence Photoacoustic Imaging—0
Complexity Analysis of an Edge Preserving CNN SAR Despeckling Algorithm—0
Distributed Evolution of Deep Autoencoders—0
Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems—0
Self-Supervised training for blind multi-frame video denoising—0
Contrastive Blind Denoising Autoencoder for Real-Time Denoising of Industrial IoT Sensor Data—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