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 1601–1625 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
SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss NetworkCode1
BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder ArchitectureCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
SAR2SAR: a semi-supervised despeckling algorithm for SAR imagesCode1
Flexible Image Denoising with Multi-layer Conditional Feature ModulationCode1
Exploiting Non-Local Priors via Self-Convolution For Highly-Efficient Image RestorationCode1
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
AR-DAE: Towards Unbiased Neural Entropy Gradient EstimationCode1
Probabilistic AutoencoderCode1
Neural Sparse Representation for Image RestorationCode1
Understanding Graph Neural Networks from Graph Signal Denoising PerspectivesCode1
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
Joint Demosaicing and Denoising With Self GuidanceCode1
Self2Self With Dropout: Learning Self-Supervised Denoising From Single ImageCode1
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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