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 6851–6875 of 7282 papers

TitleStatusHype
Learning Deep Representations Using Convolutional Auto-encoders with Symmetric Skip ConnectionsCode0
Dictionary Learning with Equiprobable Matching Pursuit—0
Times series averaging and denoising from a probabilistic perspective on time-elastic kernelsCode0
Semi-supervised Learning using Denoising Autoencoders for Brain Lesion Detection and Segmentation—0
Image Segmentation Using Overlapping Group Sparsity—0
Straight to Shapes: Real-time Detection of Encoded ShapesCode0
Learning to Distill: The Essence Vector Modeling Framework—0
Non-Local Color Image Denoising with Convolutional Neural Networks—0
Learning Fully Convolutional Networks for Iterative Non-blind Deconvolution—0
Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold SimplificationCode0
Reweighted Low-Rank Tensor Decomposition based on t-SVD and its Applications in Video Denoising—0
Mesh Denoising via Cascaded Normal Regression—0
Motion Estimated-Compensated Reconstruction with Preserved-Features in Free-Breathing Cardiac MRI—0
Can fully convolutional networks perform well for general image restoration problems?—0
The Little Engine that Could: Regularization by Denoising (RED)Code0
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks—0
Point Cloud Denoising via Moving RPCA: MRPCA—0
Stationary time-vertex signal processing—0
A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction—0
Learning Adaptive Parameter Tuning for Image Processing—0
Sparse Signal Subspace Decomposition Based on Adaptive Over-complete Dictionary—0
Automated OCT Segmentation for Images with DME—0
Multispectral image denoising with optimized vector non-local mean filter—0
Deep Identity-aware Transfer of Facial Attributes—0
Amortised MAP Inference for Image Super-resolution—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