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 6951–6975 of 7282 papers

TitleStatusHype
Beyond Brightness Constancy: Learning Noise Models for Optical Flow—0
Online Nonnegative Matrix Factorization with Outliers—0
Sublabel-Accurate Convex Relaxation of Vectorial Multilabel EnergiesCode0
A Semisupervised Approach for Language Identification based on Ladder NetworksCode0
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip ConnectionsCode0
Sweep Distortion Removal from THz Images via Blind Demodulation—0
Con-Patch: When a Patch Meets its Context—0
A Discontinuous Neural Network for Non-Negative Sparse Approximation—0
Graph Based Sinogram Denoising for Tomographic Reconstructions—0
Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence AutoencoderCode0
LiDAR Ground Filtering Algorithm for Urban Areas Using Scan Line Based Segmentation—0
Superresolution of Noisy Remotely Sensed Images Through Directional Representations—0
Patch-Ordering as a Regularization for Inverse Problems in Image ProcessingCode0
CNN for License Plate Motion Deblurring—0
Denoising and Covariance Estimation of Single Particle Cryo-EM Images—0
Deep neural networks for learning graph representations—0
EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses—0
Density-based Denoising of Point Cloud—0
Autoregressive Moving Average Graph Filtering—0
Image Restoration and Reconstruction using Variable Splitting and Class-adapted Image Priors—0
Screen Content Image Segmentation Using Sparse Decomposition and Total Variation Minimization—0
Algorithm-Induced Prior for Image Restoration—0
Iterative Gaussianization: from ICA to Random RotationsCode1
Image Denoising with Kernels based on Natural Image Relations—0
Adaptive Image Denoising by Mixture Adaptation—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