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 6851–6875 of 7282 papers

TitleStatusHype
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
Point Cloud Denoising via Moving RPCA: MRPCA—0
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks—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
Statistical Inference Using Mean Shift Denoising—0
Restoring STM images via Sparse Coding: noise and artifact removal—0
Quantum spectral analysis: frequency in time, with applications to signal and image processing—0
Neural-based Noise Filtering from Word EmbeddingsCode0
A New Data Representation Based on Training Data Characteristics to Extract Drug Named-Entity in Medical Text—0
Neural Structural Correspondence Learning for Domain AdaptationCode0
Near-Infrared Coloring via a Contrast-Preserving Mapping Model—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