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 5251–5275 of 7282 papers

TitleStatusHype
Video-Specific Autoencoders for Exploring, Editing and Transmitting Videos—0
Deep Noise Suppression With Non-Intrusive PESQNet Supervision Enabling the Use of Real Training Data—0
Low-dimensional Denoising Embedding Transformer for ECG Classification—0
Two-Stage Monte Carlo Denoising with Adaptive Sampling and Kernel Pool—0
Denoise and Contrast for Category Agnostic Shape CompletionCode1
In-Place Scene Labelling and Understanding with Implicit Scene Representation—0
Modeling Graph Node Correlations with Neighbor Mixture Models—0
Representation, Analysis of Bayesian Refinement Approximation Network: A Survey—0
Training a Task-Specific Image Reconstruction Loss—0
Multimodal Knowledge ExpansionCode1
Weakly-supervised Audio-visual Sound Source Detection and Separation—0
Task-Oriented Low-Dose CT Image DenoisingCode0
JDSR-GAN: Constructing An Efficient Joint Learning Network for Masked Face Super-Resolution—0
Patch Craft: Video Denoising by Deep Modeling and Patch MatchingCode1
Deep Learning with robustness to missing data: A novel approach to the detection of COVID-19—0
Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition—0
MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising—0
Finite Impulse Response Filters for Simplicial Complexes—0
TSTNN: Two-stage Transformer based Neural Network for Speech Enhancement in the Time Domain—0
Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image DenoiserCode1
New Computational Techniques for a Faster Variation of BM3D Image Denoising—0
Data Discovery Using Lossless Compression-Based Sparse Representation—0
Fast and Accurate: Video Enhancement using Sparse Depth—0
Exact Sparse Orthogonal Dictionary Learning—0
Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparisonCode0
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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