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 5651–5675 of 7282 papers

TitleStatusHype
Unsupervised Neural Machine Translation for English and Manipuri—0
Denoising Pre-Training and Data Augmentation Strategies for Enhanced RDF Verbalization with Transformers—0
Deep Dose Plugin Towards Real-time Monte Carlo Dose Calculation Through a Deep Learning based Denoising Algorithm—0
Adaptive noise imitation for image denoising—0
Representing and Denoising Wearable ECG Recordings—0
SAR Image Despeckling Based on Convolutional Denoising Autoencoder—0
Lattice Fusion Networks for Image Denoising—0
Gradient Descent for Deep Matrix Factorization: Dynamics and Implicit Bias towards Low Rank—0
Image Denoising for Strong Gaussian Noises With Specialized CNNs for Different Frequency Components—0
Joint Reconstruction and Calibration using Regularization by Denoising—0
Quantized Neural Networks for Radar Interference Mitigation—0
Separating and denoising seismic signals with dual-path recurrent neural network architecture—0
Distribution Conditional Denoising: A Flexible Discriminative Image Denoiser—0
Spectral Domain Spline Graph Filter Bank—0
The Gaussian Process Latent Autoregressive Model—0
Learnable Sampling 3D Convolution for Video Enhancement and Action Recognition—0
Graph Signal Recovery Using Restricted Boltzmann MachinesCode0
Image Denoising by Gaussian Patch Mixture Model and Low Rank Patches—0
Plug-And-Play Learned Gaussian-mixture Approximate Message Passing—0
Digging Deeper into CRNN Model in Chinese Text Images Recognition—0
Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation—0
Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder—0
Assessing Wireless Sensing Potential with Large Intelligent Surfaces—0
Denoising Score-Matching for Uncertainty Quantification in Inverse ProblemsCode0
Automatic artifact removal of resting-state fMRI with Deep Neural Networks—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