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 6076–6100 of 7282 papers

TitleStatusHype
Feature Enhancement with Deep Feature Losses for Speaker VerificationCode0
Noisier2Noise: Learning to Denoise from Unpaired Noisy DataCode0
Fast and Differentiable Message Passing on Pairwise Markov Random FieldsCode0
Improved functional MRI activation mapping in white matter through diffusion-adapted spatial filtering—0
Streaming Networks: Enable A Robust Classification of Noise-Corrupted Images—0
Learning Priors in High-frequency Domain for Inverse Imaging ReconstructionCode0
Mobile Recognition of Wikipedia Featured Sites using Deep Learning and Crowd-sourced Imagery—0
Learning a Generic Adaptive Wavelet Shrinkage Function for Denoising—0
Automatic Lumbar Spinal CT Image Segmentation with a Dual Densely Connected U-Net—0
AeGAN: Time-Frequency Speech Denoising via Generative Adversarial Networks—0
KRNET: Image Denoising with Kernel Regulation Network—0
Image Restoration Using Deep Regulated Convolutional NetworksCode0
Utilising Low Complexity CNNs to Lift Non-Local Redundancies in Video Coding—0
OpenDenoising: an Extensible Benchmark for Building Comparative Studies of Image DenoisersCode0
SDCNet: Smoothed Dense-Convolution Network for Restoring Low-Dose Cerebral CT PerfusionCode0
Attention Mechanism Enhanced Kernel Prediction Networks for Denoising of Burst ImagesCode0
Image Deconvolution with Deep Image and Kernel Priors—0
Electro-Magnetic Side-Channel Attack Through Learned Denoising and ClassificationCode0
Towards a Precipitation Bias Corrector against Noise and Maldistribution—0
ERNet Family: Hardware-Oriented CNN Models for Computational Imaging Using Block-Based Inference—0
Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image DenoisingCode0
eCNN: A Block-Based and Highly-Parallel CNN Accelerator for Edge Inference—0
DOA Estimation by DNN-based Denoising and Dereverberation from Sound Intensity Vector—0
Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion—0
SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition—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