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 4026–4050 of 7282 papers

TitleStatusHype
Multimodal sensor fusion in the latent representation space—0
Multimodal sparse representation learning and applications—0
Multi-mode Core Tensor Factorization based Low-Rankness and Its Applications to Tensor Completion—0
Multi-Objective Optimization for Self-Adjusting Weighted Gradient in Machine Learning Tasks—0
Multi-objective Progressive Clustering for Semi-supervised Domain Adaptation in Speaker Verification—0
Multi-Perspective Anomaly Detection—0
Multi-plane denoising diffusion-based dimensionality expansion for 2D-to-3D reconstruction of microstructures with harmonized sampling—0
Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation—0
Multiple Imputation with Denoising Autoencoder using Metamorphic Truth and Imputation Feedback—0
Multiple-Question Multiple-Answer Text-VQA—0
Multipoint Filtering with Local Polynomial Approximation and Range Guidance—0
Multi-pretrained Deep Neural Network—0
Multiresolution Dual-Polynomial Decomposition Approach for Optimized Characterization of Motor Intent in Myoelectric Control Systems—0
Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis—0
Multi-scale 2D Temporal Map Diffusion Models for Natural Language Video Localization—0
TRAIL: Transferable Robust Adversarial Images via Latent diffusion—0
Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT—0
Multiscale Adaptive Representation of Signals: I. The Basic Framework—0
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration—0
Multi-Scale Energy (MuSE) plug and play framework for inverse problems—0
Multiscale Hybrid Non-local Means Filtering Using Modified Similarity Measure—0
Multiscale Latent Diffusion Model for Enhanced Feature Extraction from Medical Images—0
Multiscale Optimal Filtering on the Sphere—0
Multi-Scale Representation Learning for Image Restoration with State-Space Model—0
Multiscale Shrinkage and Lévy Processes—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