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 5351–5375 of 7282 papers

TitleStatusHype
A Simple Yet Effective Improvement to the Bilateral Filter for Image Denoising—0
A Single Speech Enhancement Model Unifying Dereverberation, Denoising, Speaker Counting, Separation, and Extraction—0
A Spatial-Temporal Decomposition Based Deep Neural Network for Time Series Forecasting—0
A Spectrum-based Image Denoising Method with Edge Feature Enhancement—0
ASP: Learning to Forget with Adaptive Synaptic Plasticity in Spiking Neural Networks—0
A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation—0
Assessing Adversarial Replay and Deep Learning-Driven Attacks on Specific Emitter Identification-based Security Approaches—0
Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context—0
Assessing the Impact of Deep Neural Network-based Image Denoising on Binary Signal Detection Tasks—0
Assessing the performance of CT image denoisers using Laguerre-Gauss Channelized Hotelling Observer for lesion detection—0
Assessing Wireless Sensing Potential with Large Intelligent Surfaces—0
Assistive Recipe Editing through Critiquing—0
A-STAR: Test-time Attention Segregation and Retention for Text-to-image Synthesis—0
A Statistical Approach to Signal Denoising Based on Data-driven Multiscale Representation—0
A statistical physics framework for optimal learning—0
AstroClearNet: Deep image prior for multi-frame astronomical image restoration—0
Multi-Sentence Grounding for Long-term Instructional Video—0
Astronomical Image Denoising Using Dictionary Learning—0
A Study of Shape Modeling Against Noise—0
A Study on Context Length and Efficient Transformers for Biomedical Image Analysis—0
A Sub-band Approach to Deep Denoising Wavelet Networks and a Frequency-adaptive Loss for Perceptual Quality—0
A Survey on Deep learning based Document Image Enhancement—0
A Survey on Deep Tabular Learning—0
A Survey on Hyperspectral Image Restoration: From the View of Low-Rank Tensor Approximation—0
A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond—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