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 5101–5150 of 7282 papers

TitleStatusHype
MRIR: Integrating Multimodal Insights for Diffusion-based Realistic Image Restoration—0
MS-UNet-v2: Adaptive Denoising Method and Training Strategy for Medical Image Segmentation with Small Training Data—0
TQD-Track: Temporal Query Denoising for 3D Multi-Object Tracking—0
Mu^2SLAM: Multitask, Multilingual Speech and Language Models—0
Adaptive Extensions of Unbiased Risk Estimators for Unsupervised Magnetic Resonance Image Denoising—0
Multi-Architecture Multi-Expert Diffusion Models—0
Multi-band Weighted l_p Norm Minimization for Image Denoising—0
Multi-bin Trainable Linear Unit for Fast Image Restoration Networks—0
Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction—0
Multi-channel Nuclear Norm Minus Frobenius Norm Minimization for Color Image Denoising—0
Multi-Channel Speech Denoising for Machine Ears—0
Multichannel Speech Enhancement by Raw Waveform-mapping using Fully Convolutional Networks—0
Multi-Channel Swin Transformer Framework for Bearing Remaining Useful Life Prediction—0
Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising—0
MCDIP-ADMM: Overcoming Overfitting in DIP-based CT reconstruction—0
Multi-Conditioned Denoising Diffusion Probabilistic Model (mDDPM) for Medical Image Synthesis—0
Multi-Contextual Design of Convolutional Neural Network for Steganalysis—0
Multi-Cycle-Consistent Adversarial Networks for CT Image Denoising—0
Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images—0
Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation—0
Multi-Dimensional Framework for EEG Signal Processing and Denoising Through Tensor-based Architecture—0
Multidimensional TV-Stokes for image processing—0
TRACE: Trajectory-Constrained Concept Erasure in Diffusion Models—0
Multi-Domain Processing via Hybrid Denoising Networks for Speech Enhancement—0
Multi-Domain Sentiment Relevance Classification with Automatic Representation Learning—0
Denoising Large-Scale Image Captioning from Alt-text Data using Content Selection Models—0
Multi-frame denoising of high speed optical coherence tomography data using inter-frame and intra-frame priors—0
Multi-Kernel Filtering for Nonstationary Noise: An Extension of Bilateral Filtering Using Image Context—0
Multilayer Fisher extreme learning machine for classification—0
Multi-level Encoder-Decoder Architectures for Image Restoration—0
Multi-Level Generative Models for Partial Label Learning with Non-random Label Noise—0
TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation—0
Multilingual Transfer and Domain Adaptation for Low-Resource Languages of Spain—0
Multilingual Translation from Denoising Pre-Training—0
Multilingual Unsupervised Neural Machine Translation with Denoising Adapters—0
Multilingual Unsupervised NMT using Shared Encoder and Language-Specific Decoders—0
Multi-LoRA Composition for Image Generation—0
Multi-modal and frequency-weighted tensor nuclear norm for hyperspectral image denoising—0
Adaptive Estimation of Multivariate Piecewise Polynomials and Bounded Variation Functions by Optimal Decision Trees—0
Multi-Modal Contrastive Masked Autoencoders: A Two-Stage Progressive Pre-training Approach for RGBD Datasets—0
Effective Probabilistic Time Series Forecasting with Fourier Adaptive Noise-Separated Diffusion—0
Multimodal Data Visualization and Denoising with Integrated Diffusion—0
Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing—0
Multimodal-driven Talking Face Generation via a Unified Diffusion-based Generator—0
Multimodal Graph Neural Network for Recommendation with Dynamic De-redundancy and Modality-Guided Feature De-noisy—0
Multimodal Image Denoising based on Coupled Dictionary Learning—0
Multi-modal Image Processing based on Coupled Dictionary Learning—0
Learning Patterns in Sample Distributions for Monte Carlo Variance Reduction—0
Multi-modal Pose Diffuser: A Multimodal Generative Conditional Pose Prior—0
Multimodal sensor fusion in the latent representation space—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