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 1601–1650 of 7282 papers

TitleStatusHype
Dual Adversarial Network: Toward Real-world Noise Removal and Noise GenerationCode1
Lightweight Modules for Efficient Deep Learning based Image RestorationCode1
Designing and Training of A Dual CNN for Image DenoisingCode1
Learning Graph-Convolutional Representations for Point Cloud DenoisingCode1
Accelerated MRI with Un-trained Neural NetworksCode1
Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural NetworksCode1
Joint Frequency and Image Space Learning for MRI Reconstruction and AnalysisCode1
BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder ArchitectureCode1
SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss NetworkCode1
SAR2SAR: a semi-supervised despeckling algorithm for SAR imagesCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
Exploiting Non-Local Priors via Self-Convolution For Highly-Efficient Image RestorationCode1
Flexible Image Denoising with Multi-layer Conditional Feature ModulationCode1
Image Deconvolution via Noise-Tolerant Self-Supervised InversionCode1
HiFi-GAN: High-Fidelity Denoising and Dereverberation Based on Speech Deep Features in Adversarial NetworksCode1
Fully Unsupervised Diversity Denoising with Convolutional Variational AutoencodersCode1
Probabilistic AutoencoderCode1
AR-DAE: Towards Unbiased Neural Entropy Gradient EstimationCode1
Understanding Graph Neural Networks from Graph Signal Denoising PerspectivesCode1
Neural Sparse Representation for Image RestorationCode1
Denoising Implicit Feedback for RecommendationCode1
A zero-inflated gamma model for deconvolved calcium imaging tracesCode1
Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting SurfacesCode1
Self2Self With Dropout: Learning Self-Supervised Denoising From Single ImageCode1
Joint Demosaicing and Denoising With Self GuidanceCode1
Lite Audio-Visual Speech EnhancementCode1
RARE: Image Reconstruction using Deep Priors Learned without Ground TruthCode1
PMHLD: Patch Map Based Hybrid Learning DehazeNet for Single Image Haze RemovalCode1
TTS-Portuguese Corpus: a corpus for speech synthesis in Brazilian PortugueseCode1
Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximationCode1
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid DecoderCode1
DenoiSeg: Joint Denoising and SegmentationCode1
Exploring the Loss Landscape in Neural Architecture SearchCode1
Comparison of Image Quality Models for Optimization of Image Processing SystemsCode1
SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss SmoothingCode1
Pyramid Attention Networks for Image RestorationCode1
Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoderCode1
Microscopy Image Restoration using Deep Learning on W2SCode1
Learning an Adaptive Model for Extreme Low-light Raw Image ProcessingCode1
AMP-Net: Denoising based Deep Unfolding for Compressive Image SensingCode1
Unsupervised Opinion Summarization with Noising and DenoisingCode1
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned GenerationCode1
Learning from Rules Generalizing Labeled ExemplarsCode1
Test-Time Adaptable Neural Networks for Robust Medical Image SegmentationCode1
WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-end Speech EnhancementCode1
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New StrategyCode1
Supervised Raw Video Denoising with a Benchmark Dataset on Dynamic ScenesCode1
Flows for simultaneous manifold learning and density estimationCode1
Plug-and-Play Algorithms for Large-scale Snapshot Compressive ImagingCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
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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