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 6326–6350 of 7282 papers

TitleStatusHype
Tractable Approach to MmWaves Cellular Analysis with FSO Backhauling under Feedback Delay and Hardware LimitationsCode0
Denoising Prior Driven Deep Neural Network for Image RestorationCode0
Foundation Models For Seismic Data Processing: An Extensive ReviewCode0
Denoising quantum states with Quantum Autoencoders -- Theory and ApplicationsCode0
FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural NetworksCode0
A Brief Review of Real-World Color Image DenoisingCode0
Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic SegmentationCode0
KADEL: Knowledge-Aware Denoising Learning for Commit Message GenerationCode0
Denoising Score-Matching for Uncertainty Quantification in Inverse ProblemsCode0
Multi-head Sequence Tagging Model for Grammatical Error CorrectionCode0
A Flag Decomposition for Hierarchical DatasetsCode0
Multi-hypotheses Conditioned Point Cloud Diffusion for 3D Human Reconstruction from Occluded ImagesCode0
Convolutional Neural Networks Can Be Deceived by Visual IllusionsCode0
Multi-Kernel Prediction Networks for Denoising of Burst ImagesCode0
Class-Aware Fully-Convolutional Gaussian and Poisson DenoisingCode0
CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature DenoiserCode0
Denoising Table-Text Retrieval for Open-Domain Question AnsweringCode0
pcaGAN: Improving Posterior-Sampling cGANs via Principal Component RegularizationCode0
Multi-Level Sequence Denoising with Cross-Signal Contrastive Learning for Sequential RecommendationCode0
FreeDiff: Progressive Frequency Truncation for Image Editing with Diffusion ModelsCode0
Multi-level Wavelet-CNN for Image RestorationCode0
Multi-level Wavelet Convolutional Neural NetworksCode0
Analysis of learning a flow-based generative model from limited sample complexityCode0
SPD-DDPM: Denoising Diffusion Probabilistic Models in the Symmetric Positive Definite SpaceCode0
Denoising Variational Graph of Graphs Auto-Encoder for Predicting Structured Entity InteractionsCode0
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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