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

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Showing 201–250 of 1220 papers

TitleStatusHype
PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image DenoisingCode1
CNN-based InSAR Denoising and Coherence MetricCode1
Learning Enriched Features for Real Image Restoration and EnhancementCode1
DCT2net: an interpretable shallow CNN for image denoisingCode1
Random Sub-Samples Generation for Self-Supervised Real Image DenoisingCode1
GroupCDL: Interpretable Denoising and Compressed Sensing MRI via Learned Group-Sparsity and Circulant AttentionCode1
HINet: Half Instance Normalization Network for Image RestorationCode1
COLA-Net: Collaborative Attention Network for Image RestorationCode1
Learning Medical Image Denoising with Deep Dynamic Residual Attention NetworkCode1
High Perceptual Quality Image Denoising with a Posterior Sampling CGANCode1
Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image DenoisingCode1
Color Image Denoising Using The Green Channel PriorCode1
Asymmetric Mask Scheme for Self-Supervised Real Image DenoisingCode1
Recurrent Inference Machines for Solving Inverse ProblemsCode1
Probabilistic Noise2Void: Unsupervised Content-Aware DenoisingCode1
CycleISP: Real Image Restoration via Improved Data SynthesisCode1
A cross Transformer for image denoisingCode1
Representing Noisy Image Without DenoisingCode1
Restormer: Efficient Transformer for High-Resolution Image RestorationCode1
Rethinking Noise Synthesis and Modeling in Raw DenoisingCode1
Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video DenoisingCode1
Hyperspectral Image Denoising Using SURE-Based Unsupervised Convolutional Neural NetworksCode1
3D Quasi-Recurrent Neural Network for Hyperspectral Image DenoisingCode1
Learning Degradation Representations for Image DeblurringCode1
IDR: Self-Supervised Image Denoising via Iterative Data RefinementCode1
Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-CaseCode1
Content-Noise Complementary Learning for Medical Image DenoisingCode1
Image Denoising and the Generative Accumulation of PhotonsCode1
Image Denoising Using Green Channel PriorCode1
Scientific Image Restoration AnywhereCode1
Deep Image PriorCode1
DDT: Dual-branch Deformable Transformer for Image DenoisingCode1
Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical FlowCode1
Self2Self With Dropout: Learning Self-Supervised Denoising From Single ImageCode1
Image Restoration via Frequency SelectionCode1
Implementation of the VBM3D Video Denoising Method and Some VariantsCode1
Disentangling Noise from Images: A Flow-Based Image Denoising Neural NetworkCode1
Single-exposure absorption imaging of ultracold atoms using deep learningCode1
CT-Mamba: A Hybrid Convolutional State Space Model for Low-Dose CT DenoisingCode1
Interpreting Low-level Vision Models with Causal Effect MapsCode1
Kornia: an Open Source Differentiable Computer Vision Library for PyTorchCode1
Invertible Denoising Network: A Light Solution for Real Noise RemovalCode1
Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-ExpertsCode1
ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image PriorCode1
Joint Frequency and Image Space Learning for MRI Reconstruction and AnalysisCode1
Joint Low Dose CT Denoising And Kidney SegmentationCode1
CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from ImageCode1
LAN: Learning to Adapt Noise for Image DenoisingCode1
As if by magic: self-supervised training of deep despeckling networks with MERLINCode1
From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image RestorationCode1
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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
10MIRNetPSNR (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