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 6076–6100 of 7282 papers

TitleStatusHype
Self-Induced Curriculum Learning in Neural Machine Translation—0
Samples Are Useful? Not Always: denoising policy gradient updates using variance explained—0
Isolating Latent Structure with Cross-population Variational Autoencoders—0
BOOSTING ENCODER-DECODER CNN FOR INVERSE PROBLEMS—0
Denoising Improves Latent Space Geometry in Text Autoencoders—0
Manifold Modeling in Embedded Space: A Perspective for Interpreting "Deep Image Prior"—0
Annealed Denoising score matching: learning Energy based model in high-dimensional spaces—0
Learning in Confusion: Batch Active Learning with Noisy Oracle—0
IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRICode0
Adapting Language Models for Non-Parallel Author-Stylized Rewriting—0
Nonlocal Patches based Gaussian Mixture Model for Image Inpainting—0
Deep Message Passing on Sets—0
Infusing Learned Priors into Model-Based Multispectral Imaging—0
Memory-Efficient Hierarchical Neural Architecture Search for Image DenoisingCode1
Unsupervised Sketch-to-Photo SynthesisCode1
On reconstruction algorithms for signals sparse in Hermite and Fourier domains—0
Properties of Laplacian Pyramids for Extension and Denoising—0
Interpretable and robust blind image denoising with bias-free convolutional neural networks—0
PatchDIP Exploiting Patch Redundancy in Deep Image Prior for Denoising—0
Generative Models for Low-Dimensional Video Representation and Compressive Sensing—0
Document Enhancement System Using Auto-encoders—0
Energy Dissipation with Plug-and-Play Priors—0
Phase Retrieval using Untrained Neural Network Priors—0
Performance Analysis of Spatial and Transform Filters for Efficient Image Noise Reduction—0
Revealing Stable and Unstable Modes of Generic Denoisers through Nonlinear Eigenvalue Analysis—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