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 4726–4750 of 7282 papers

TitleStatusHype
Online Deep Learning based on Auto-Encoder—0
Improving Neural Machine Translation by Denoising Training—0
Joint denoising and HDR for RAW video sequences—0
Diffusion Models Using a Single Equation—0
Convolutional Neural Networks for Spherical Signal Processing via Spherical Haar Tight Framelets—0
Machine Learning Enhances Algorithms for Quantifying Non-Equilibrium Dynamics in Correlation Spectroscopy Experiments to Reach Frame-Rate-Limited Time Resolution—0
Predicting waves in fluids with deep neural network—0
Improving the quality control of seismic data through active learning—0
ULF: Cross-Validation for Weak Supervision—0
S5 Framework: A Review of Self-Supervised Shared Semantic Space Optimization for Multimodal Zero-Shot Learning—0
DEEP: DEnoising Entity Pre-training for Neural Machine Translation—0
Representation Learning for Resource-Constrained Keyphrase Generation—0
Balancing the Style-Content Trade-Off in Sentiment Transfer UsingPolarity-Aware Denoising—0
On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descent—0
ALA: Naturalness-aware Adversarial Lightness Attack—0
Adaptive Deep PnP Algorithm for Video Snapshot Compressive ImagingCode1
Towards Adversarially Robust Deep Image Denoising—0
PromptBERT: Improving BERT Sentence Embeddings with PromptsCode2
MoViDNN: A Mobile Platform for Evaluating Video Quality Enhancement with Deep Neural NetworksCode0
MDPose: Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler Signatures—0
An Introduction to Autoencoders—0
Image quality measurements and denoising using Fourier Ring CorrelationsCode0
Learning to Denoise Raw Mobile UI Layouts for Improving Datasets at ScaleCode1
Model-Based Image Signal Processors via Learnable Dictionaries—0
Permuted and Unlinked Monotone Regression in R^d: an approach based on mixture modeling and optimal transport—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