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 5751–5800 of 7282 papers

TitleStatusHype
Wavelet based multivariate signal denoising using Mahalanobis distance and EDF statistics—0
RARE: Image Reconstruction using Deep Priors Learned without Ground TruthCode1
Revisiting Role of Autoencoders in Adversarial Settings—0
An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challengesCode0
Attention-based network for low-light image enhancement—0
Learning Equations from Biological Data with Limited Time SamplesCode0
Self-supervised Dynamic CT Perfusion Image Denoising with Deep Neural Networks—0
Inverse problems with second-order Total Generalized Variation constraints—0
One Size Fits All: Can We Train One Denoiser for All Noise Levels?—0
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
PMHLD: Patch Map Based Hybrid Learning DehazeNet for Single Image Haze RemovalCode1
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
TTS-Portuguese Corpus: a corpus for speech synthesis in Brazilian PortugueseCode1
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
Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximationCode1
Encoding in the Dark Grand Challenge: An Overview—0
Exploring the Loss Landscape in Neural Architecture SearchCode1
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid DecoderCode1
DenoiSeg: Joint Denoising and SegmentationCode1
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
Comparison of Image Quality Models for Optimization of Image Processing SystemsCode1
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
Learning to Rank Intents in Voice Assistants—0
Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders—0
SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss SmoothingCode1
Pyramid Attention Networks for Image RestorationCode1
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
Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoderCode1
Identity Enhanced Residual Image DenoisingCode0
Attention Based Real Image RestorationCode0
Kalman Filter and Wavelet Cross-correlation for VHF Broadband Interferometer Lightning Mapping—0
Deep Photon Mapping—0
Accurate Graph Filtering in Wireless Sensor Networks—0
A Review of an Old Dilemma: Demosaicking First, or Denoising First?—0
Virtual SAR: A Synthetic Dataset for Deep Learning based Speckle Noise Reduction Algorithms—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
Microscopy Image Restoration using Deep Learning on W2SCode1
Learning an Adaptive Model for Extreme Low-light Raw Image ProcessingCode1
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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