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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4901–4925 of 7282 papers

TitleStatusHype
Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT—0
An Interpretable Joint Nonnegative Matrix Factorization-Based Point Cloud Distance Measure—0
Denoising single images by feature ensemble revisited—0
PUF-Phenotype: A Robust and Noise-Resilient Approach to Aid Intra-Group-based Authentication with DRAM-PUFs Using Machine Learning—0
Spatiotemporal singular value decomposition for denoising in photoacoustic imaging with low-energy excitation light source—0
Rank-Enhanced Low-Dimensional Convolution Set for Hyperspectral Image Denoising—0
Spatio-temporal error concealment in video by denoised temporal extrapolation refinement—0
Efficient Pruning for Machine Learning Under Homomorphic Encryption—0
DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning—0
Test-time Adaptation for Real Image Denoising via Meta-transfer Learning—0
Towards Real-World Video Denosing: A Practical Video Denosing Dataset and Network—0
WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer—0
Polarized Color Image Denoising using Pocoformer—0
SPI-GAN: Denoising Diffusion GANs with Straight-Path Interpolations—0
Theoretical Perspectives on Deep Learning Methods in Inverse Problems—0
DeStripe: A Self2Self Spatio-Spectral Graph Neural Network with Unfolded Hessian for Stripe Artifact Removal in Light-sheet Microscopy—0
Noise-aware Physics-informed Machine Learning for Robust PDE DiscoveryCode0
Defense against adversarial attacks on deep convolutional neural networks through nonlocal denoising—0
Megapixel Image Generation with Step-Unrolled Denoising AutoencodersCode0
Using Autoencoders on Differentially Private Federated Learning GANsCode0
Self-Supervised Training with Autoencoders for Visual Anomaly Detection—0
Speaker-Independent Microphone Identification in Noisy Conditions—0
On Grid Compressive Sampling for Spherical Field Measurements in Acoustics—0
KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP—0
SJ-HD^2R: Selective Joint High Dynamic Range and Denoising Imaging for Dynamic Scenes—0
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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