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 6326–6350 of 7282 papers

TitleStatusHype
DenseFormer: Learning Dense Depth Map from Sparse Depth and Image via Conditional Diffusion Model—0
DensePure: Understanding Diffusion Models towards Adversarial Robustness—0
Dense residual Transformer for image denoising—0
Dense-Sparse Deep Convolutional Neural Networks Training for Image Denoising—0
Density—0
Density-based Denoising of Point Cloud—0
Depth-guided NeRF Training via Earth Mover's Distance—0
Depth image denoising using nuclear norm and learning graph model—0
Depth Map Denoising Network and Lightweight Fusion Network for Enhanced 3D Face Recognition—0
A Multi-Modal Unsupervised Machine Learning Approach for Biomedical Signal Processing in CPR—0
Video Anomaly Detection Using Pre-Trained Deep Convolutional Neural Nets and Context Mining—0
De-SaTE: Denoising Self-attention Transformer Encoders for Li-ion Battery Health Prognostics—0
Descanning: From Scanned to the Original Images with a Color Correction Diffusion Model—0
DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images—0
Design a Metric Robust to Complicated High Dimensional Noise for Efficient Manifold Denoising—0
Design Editing for Offline Model-based Optimization—0
Design Exploration of Hybrid CMOS-OxRAM Deep Generative Architectures—0
Designing A Composite Dictionary Adaptively From Joint Examples—0
Video Denoising and Enhancement via Dynamic Video Layering—0
De-speckling of Optical Coherence Tomography Images Using Anscombe Transform and a Noisier2noise Model—0
DeStripe: A Self2Self Spatio-Spectral Graph Neural Network with Unfolded Hessian for Stripe Artifact Removal in Light-sheet Microscopy—0
SRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data—0
Video Denoising in Fluorescence Guided Surgery—0
Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction—0
Details Preserving Deep Collaborative Filtering-Based Method for Image Denoising—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