SOTAVerified

Image Denoising

Image Denoising is a computer vision task that involves removing noise from an image. Noise can be introduced into an image during acquisition or processing, and can reduce image quality and make it difficult to interpret. Image denoising techniques aim to restore an image to its original quality by reducing or removing the noise, while preserving the important features of the image.

( Image credit: Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–300 of 1220 papers

TitleStatusHype
Pyramid Attention Networks for Image RestorationCode1
Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoderCode1
Supervised Raw Video Denoising with a Benchmark Dataset on Dynamic ScenesCode1
A Physics-based Noise Formation Model for Extreme Low-light Raw DenoisingCode1
GAN-based Priors for Quantifying UncertaintyCode1
CycleISP: Real Image Restoration via Improved Data SynthesisCode1
Stochastic Frequency Masking to Improve Super-Resolution and Denoising NetworksCode1
Learning Enriched Features for Real Image Restoration and EnhancementCode1
3D Quasi-Recurrent Neural Network for Hyperspectral Image DenoisingCode1
Single-exposure absorption imaging of ultracold atoms using deep learningCode1
Self-Supervised Poisson-Gaussian DenoisingCode1
Noise2Inverse: Self-supervised deep convolutional denoising for tomographyCode1
Spatial-Adaptive Network for Single Image DenoisingCode1
Ada-LISTA: Learned Solvers Adaptive to Varying ModelsCode1
CNN-based InSAR Denoising and Coherence MetricCode1
Implementation of the VBM3D Video Denoising Method and Some VariantsCode1
Scale-wise Convolution for Image RestorationCode1
Scientific Image Restoration AnywhereCode1
Kornia: an Open Source Differentiable Computer Vision Library for PyTorchCode1
Memory-Efficient Hierarchical Neural Architecture Search for Image DenoisingCode1
Variational Denoising Network: Toward Blind Noise Modeling and RemovalCode1
Noisy-As-Clean: Learning Self-supervised Denoising from the Corrupted ImageCode1
Probabilistic Noise2Void: Unsupervised Content-Aware DenoisingCode1
GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world Noise ModelingCode1
Learning Deformable Kernels for Image and Video DenoisingCode1
Unprocessing Images for Learned Raw DenoisingCode1
Noise2Void - Learning Denoising from Single Noisy ImagesCode1
Fully Convolutional Pixel Adaptive Image DenoiserCode1
Toward Convolutional Blind Denoising of Real PhotographsCode1
From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image RestorationCode1
Learning to See in the DarkCode1
Deep Image PriorCode1
Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual LossCode1
Recurrent Inference Machines for Solving Inverse ProblemsCode1
On-Demand Learning for Deep Image RestorationCode1
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image DenoisingCode1
Learning Multi-scale Spatial-frequency Features for Image Denoising—0
A Real-time Endoscopic Image Denoising System—0
Revisiting Transformers with Insights from Image Filtering—0
Pseudo-Siamese Blind-Spot Transformers for Self-Supervised Real-World DenoisingCode0
YOND: Practical Blind Raw Image Denoising Free from Camera-Specific Data Dependency—0
A Poisson-Guided Decomposition Network for Extreme Low-Light Image Enhancement—0
Unrolling Nonconvex Graph Total Variation for Image Denoising—0
STAR-Net: An Interpretable Model-Aided Network for Remote Sensing Image DenoisingCode0
Image denoising as a conditional expectation—0
Conformal Bounds on Full-Reference Image Quality for Imaging Inverse ProblemsCode0
Towards order of magnitude X-ray dose reduction in breast cancer imaging using phase contrast and deep denoisingCode0
Edge-preserving Image Denoising via Multi-scale Adaptive Statistical Independence Testing—0
Self-Supervised Noise Adaptive MRI Denoising via Repetition to Repetition (Rep2Rep) Learning—0
ECGDeDRDNet: A deep learning-based method for Electrocardiogram noise removal using a double recurrent dense network—0
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All datasetsSIDDDNDELD SonyA7S2 x200SID SonyA7S2 x250ELD SonyA7S2 x100SID x100SID x300SID SonyA7S2 x100urban100 sigma15Urban100 sigma50BSD68 sigma50SID SonyA7S2 x300

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CGNetPSNR (sRGB)40.39—Unverified
2KBNetPSNR (sRGB)40.35—Unverified
3NAFNetPSNR (sRGB)40.3—Unverified
4SSAMANPSNR (sRGB)40.08—Unverified
5PNGANPSNR (sRGB)40.07—Unverified
6RestormerPSNR (sRGB)40.02—Unverified
7HINetPSNR (sRGB)39.99—Unverified
8MAXIM-3SPSNR (sRGB)39.96—Unverified
9Uformer-BPSNR (sRGB)39.89—Unverified
10SRMNetPSNR (sRGB)39.72—Unverified
#ModelMetricClaimedVerifiedStatus
1DualDnPSNR (sRGB)40.59—Unverified
2PNGANPSNR (sRGB)40.18—Unverified
3SSAMANPSNR (sRGB)40.05—Unverified
4RestormerPSNR (sRGB)40.03—Unverified
5Uformer-BPSNR (sRGB)39.98—Unverified
6NBNetPSNR (sRGB)39.89—Unverified
7MIRNetPSNR (sRGB)39.88—Unverified
8MAXIM-3SPSNR (sRGB)39.84—Unverified
9MPRNetPSNR (sRGB)39.8—Unverified
10SADNetPSNR (sRGB)39.59—Unverified
#ModelMetricClaimedVerifiedStatus
1LLD*PSNR (Raw)44.95—Unverified
2PMNPSNR (Raw)44.51—Unverified
3SFRNPSNR (Raw)44.1—Unverified
4LLDPSNR (Raw)43.84—Unverified
5ELDPSNR (Raw)43.43—Unverified
6LRDPSNR (Raw)43.32—Unverified
7Paired Data(SID)PSNR (Raw)41.97—Unverified
8StarlightPSNR (Raw)40.86—Unverified
9ExposureDiffusion (UNet+ELD)PSNR (Raw)40.39—Unverified
10Noise FlowPSNR (Raw)39.23—Unverified
#ModelMetricClaimedVerifiedStatus
1LLD*PSNR (Raw)41.02—Unverified
2PMNPSNR (Raw)40.92—Unverified
3SFRNPSNR (Raw)40.22—Unverified
4LLDPSNR (Raw)39.76—Unverified
5Paired Data (SID)PSNR (Raw)39.6—Unverified
6ELDPSNR (Raw)39.44—Unverified
7LEDPSNR (Raw)39.34—Unverified
8LRDPSNR (Raw)39.25—Unverified
9StarlightPSNR (Raw)36.25—Unverified
10Noise FlowPSNR (Raw)35.8—Unverified
#ModelMetricClaimedVerifiedStatus
1LLD*PSNR (Raw)46.74—Unverified
2PMNPSNR (Raw)46.5—Unverified
3SFRNPSNR (Raw)46.02—Unverified
4LLDPSNR (Raw)45.61—Unverified
5ELDPSNR (Raw)45.45—Unverified
6LRDPSNR (Raw)44.95—Unverified
7Paired Data(SID)PSNR (Raw)44.47—Unverified
8StarlightPSNR (Raw)43.8—Unverified
9Noise FlowPSNR (Raw)41.05—Unverified
#ModelMetricClaimedVerifiedStatus
1LLD*PSNR (Raw)43.36—Unverified
2PMNPSNR (Raw)43.16—Unverified
3SFRNPSNR (Raw)42.29—Unverified
4LLDPSNR (Raw)42.1—Unverified
5SID (paired real data)PSNR (Raw)42.06—Unverified
6ELDPSNR (Raw)41.95—Unverified
7StarlightPSNR (Raw)40.47—Unverified
8Noise FlowPSNR (Raw)38.89—Unverified
#ModelMetricClaimedVerifiedStatus
1LLD*PSNR (Raw)37.8—Unverified
2PMNPSNR (Raw)37.77—Unverified
3SFRNPSNR (Raw)36.87—Unverified
4Paired Data(SID)PSNR (Raw)36.85—Unverified
5LLDPSNR (Raw)36.76—Unverified
6ELDPSNR (Raw)36.36—Unverified
7StarlightPSNR (Raw)32.99—Unverified
8Noise FlowPSNR (Raw)32.29—Unverified
#ModelMetricClaimedVerifiedStatus
1PMNPSNR (Raw)43.16—Unverified
2SFRNPSNR (Raw)42.29—Unverified
3LEDPSNR (Raw)41.98—Unverified
4ELDPSNR (Raw)41.95—Unverified
5LRDPSNR (Raw)41.95—Unverified
#ModelMetricClaimedVerifiedStatus
1AKDTAverage PSNR35.64—Unverified
2MaIR+PSNR35.42—Unverified
3MaIRPSNR35.35—Unverified
4SCUNet SCUNetAverage PSNR35.18—Unverified
#ModelMetricClaimedVerifiedStatus
1MaIR+PSNR30.41—Unverified
2MaIRPSNR30.3—Unverified
3SCUNet SCUNetPSNR30.14—Unverified
4AKDTPSNR29.82—Unverified
#ModelMetricClaimedVerifiedStatus
1MaIR+PSNR30.08—Unverified
2MaIRPSNR28.66—Unverified
#ModelMetricClaimedVerifiedStatus
1LEDPSNR (Raw)36.67—Unverified
2LRDPSNR (Raw)36.03—Unverified
#ModelMetricClaimedVerifiedStatus
1MaIR+PSNR33.3—Unverified
2MaIRPSNR33.22—Unverified
#ModelMetricClaimedVerifiedStatus
1R3LPSNR27.67—Unverified
#ModelMetricClaimedVerifiedStatus
1BRGMLPIPS0.24—Unverified
#ModelMetricClaimedVerifiedStatus
1BRGMLPIPS0.24—Unverified
#ModelMetricClaimedVerifiedStatus
1ExposureDiffusion (UNet+paired data)PSNR (Raw)36.82—Unverified
#ModelMetricClaimedVerifiedStatus
1PNGANPSNR40.78—Unverified
#ModelMetricClaimedVerifiedStatus
1PNGANPSNR40.55—Unverified
#ModelMetricClaimedVerifiedStatus
1absDLODRMSE0.07—Unverified