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 2426–2450 of 7282 papers

TitleStatusHype
Depth Completion Using a View-constrained Deep Prior—0
Automated Data Denoising for Recommendation—0
Unsupervised parameter selection for denoising with the elastic net—0
A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising—0
Evolutionary Variational Optimization of Generative Models—0
Deep Denoising: Rate-Optimal Recovery of Structured Signals with a Deep Prior—0
Rate-Optimal Denoising with Deep Neural Networks—0
A Learning-from-noise Dilated Wide Activation Network for denoising Arterial Spin Labeling (ASL) Perfusion Images—0
Deep Denoising Prior-Based Spectral Estimation for Phaseless Synthetic Aperture Radar—0
Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments—0
Two-stage dimension reduction for noisy high-dimensional images and application to Cryogenic Electron Microscopy—0
Deep Denoising Method for Side Scan Sonar Images without High-quality Reference Data—0
Automated Channel Pruning with Learned Importance—0
DEEP: DEnoising Entity Pre-training for Neural Machine Translation—0
Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network—0
EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm—0
DEEP: DEnoising Entity Pre-training for Neural Machine Translation—0
Deep Denoising Auto-encoder for Statistical Speech Synthesis—0
A Large Language Model and Denoising Diffusion Framework for Targeted Design of Microstructures with Commands in Natural Language—0
Deep denoising autoencoder-based non-invasive blood flow detection for arteriovenous fistula—0
Autoencoding Random Forests—0
A Label-Free and Non-Monotonic Metric for Evaluating Denoising in Event Cameras—0
EventDiff: A Unified and Efficient Diffusion Model Framework for Event-based Video Frame Interpolation—0
Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion—0
A Brief Survey of Recent Edge-Preserving Smoothing Algorithms on Digital Images—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