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 2126–2150 of 7282 papers

TitleStatusHype
Beyond Photo Realism for Domain Adaptation from Synthetic Data—0
Denoising Autoencoders for Overgeneralization in Neural Networks—0
Amortised MAP Inference for Image Super-resolution—0
Denoising Autoencoder-based Defensive Distillation as an Adversarial Robustness Algorithm—0
Beyond Oversmoothing: Evaluating DDPM and MSE for Scalable Speech Synthesis in ASR—0
Global Adaptive Filtering Layer for Computer Vision—0
Denoising Attention for Query-aware User Modeling in Personalized Search—0
Denoising Atmospheric Temperature Measurements Taken by the Mars Science Laboratory on the Martian Surface—0
Denoising Arterial Spin Labeling Cerebral Blood Flow Images Using Deep Learning—0
Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models—0
A Modified PINN Approach for Identifiable Compartmental Models in Epidemiology with Applications to COVID-19—0
Dual-domain Collaborative Denoising for Social Recommendation—0
Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learning—0
Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising—0
Denoising and Selecting Pseudo-Heatmaps for Semi-Supervised Human Pose Estimation—0
Denoising and Segmentation of Epigraphical Scripts—0
Beyond Generation: A Diffusion-based Low-level Feature Extractor for Detecting AI-generated Images—0
Denoising and Reconstruction of Nonlinear Dynamics using Truncated Reservoir Computing—0
A Missing Information Loss function for implicit feedback datasets—0
Denoising and Optical and SAR Image Classifications Based on Feature Extraction and Sparse Representation—0
Beyond Fixed Horizons: A Theoretical Framework for Adaptive Denoising Diffusions—0
Accelerating Diffusion-based Combinatorial Optimization Solvers by Progressive Distillation—0
Denoising and feature extraction in photoemission spectra with variational auto-encoder neural networks—0
Denoising and Covariance Estimation of Single Particle Cryo-EM Images—0
A method of limiting performance loss of CNNs in noisy environments—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