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 6876–6900 of 7282 papers

TitleStatusHype
Nonsymbolic Text Representation—0
Low-dose CT denoising with convolutional neural network—0
Retrieval Term Prediction Using Deep Learning Methods—0
Tensor Based Second Order Variational Model for Image Reconstruction—0
Blind Facial Image Quality Enhancement using Non-Rigid Semantic Patches—0
Image Denoising via Multi-scale Nonlinear Diffusion Models—0
Poisson Noise Reduction with Higher-order Natural Image Prior Model—0
Recursive nearest agglomeration (ReNA): fast clustering for approximation of structured signalsCode0
Learning Sparse Graphs Under Smoothness Prior—0
Image denoising via group sparsity residual constraint—0
Rectifier Neural Network with a Dual-Pathway Architecture for Image Denoising—0
Image Denoising Via Collaborative Support-Agnostic Recovery—0
An empirical study on the effects of different types of noise in image classification tasksCode0
Adaptive Regularization in Convex Composite Optimization for Variational Imaging Problems—0
Guided Filter based Edge-preserving Image Non-blind Deconvolution—0
An Adaptive Parameter Estimation for Guided Filter based Image Deconvolution—0
Convexified Convolutional Neural NetworksCode0
MindX: Denoising Mixed Impulse Poisson-Gaussian Noise Using Proximal Algorithms—0
Bayesian selection for the l2-Potts model regularization parameter: 1D piecewise constant signal denoising—0
A Non-Local Conventional Approach for Noise Removal in 3D MRI—0
Parameter Learning for Log-supermodular Distributions—0
Medical image denoising using convolutional denoising autoencodersCode0
A Comparative Study for the Nuclear Norms Minimization Methods—0
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image DenoisingCode1
Fractional Calculus In Image Processing: A Review—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