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 4401–4425 of 7282 papers

TitleStatusHype
Averaged Deep Denoisers for Image RegularizationCode0
Towards Better Dermoscopic Image Feature Representation Learning for Melanoma ClassificationCode0
Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT—0
Real-time Streaming Video Denoising with Bidirectional BuffersCode1
Learnability Enhancement for Low-light Raw Denoising: Where Paired Real Data Meets Noise ModelingCode1
ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-SpeechCode3
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
Rank-Enhanced Low-Dimensional Convolution Set for Hyperspectral Image Denoising—0
Spatiotemporal singular value decomposition for denoising in photoacoustic imaging with low-energy excitation light source—0
Spatio-temporal error concealment in video by denoised temporal extrapolation refinement—0
D2HNet: Joint Denoising and Deblurring with Hierarchical Network for Robust Night Image RestorationCode1
DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning—0
Efficient Pruning for Machine Learning Under Homomorphic Encryption—0
Patch-wise Deep Metric Learning for Unsupervised Low-Dose CT DenoisingCode1
Don't Pay Attention to the Noise: Learning Self-supervised Representations of Light Curves with a Denoising Time Series TransformerCode1
Test-time Adaptation for Real Image Denoising via Meta-transfer Learning—0
Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super ResolutionCode1
Towards Real-World Video Denosing: A Practical Video Denosing Dataset and Network—0
Variational Deep Image RestorationCode1
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
AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex NoiseCode2
Semantic Image Synthesis via Diffusion ModelsCode2
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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