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 7026–7050 of 7282 papers

TitleStatusHype
Computational Intractability of Dictionary Learning for Sparse Representation—0
Prediction of Dynamical time Series Using Kernel Based Regression and Smooth Splines—0
Postprocessing of Compressed Images via Sequential Denoising—0
Spectral Convergence Rate of Graph Laplacian—0
Color graph based wavelet transform with perceptual information—0
On 1-Laplacian Elliptic Equations Modeling Magnetic Resonance Image Rician Denoising—0
Fast and Accurate Poisson Denoising with Optimized Nonlinear Diffusion—0
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection—0
Bregman Iteration for Correspondence Problems: A Study of Optical Flow—0
Japanese Sentiment Classification with Stacked Denoising Auto-Encoder using Distributed Word Representation—0
Non-linear prediction with LSTM recurrent neural networks for acoustic novelty detection—0
Unsupervised Domain Adaptation for Word Sense Disambiguation using Stacked Denoising Autoencoder—0
Estimating network edge probabilities by neighborhood smoothingCode0
Denoising without access to clean data using a partitioned autoencoder—0
Precise Phase Transition of Total Variation Minimization—0
DeepSat - A Learning framework for Satellite ImageryCode0
Edge-enhancing Filters with Negative Weights—0
Accelerated graph-based spectral polynomial filters—0
Conjugate Gradient Acceleration of Non-Linear Smoothing Filters—0
Chebyshev and Conjugate Gradient Filters for Graph Image Denoising—0
Closing the Gap: Domain Adaptation from Explicit to Implicit Discourse Relations—0
Approximate Nearest Neighbor Fields in Video—0
Mixed Gaussian-Impulse Noise Removal from Highly Corrupted Images via Adaptive Local and Nonlocal Statistical Priors—0
A Deep Learning Approach to Structured Signal Recovery—0
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration—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