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 5001–5050 of 7282 papers

TitleStatusHype
MIMO Speech Compression and Enhancement Based on Convolutional Denoising Autoencoder—0
MindX: Denoising Mixed Impulse Poisson-Gaussian Noise Using Proximal Algorithms—0
MING: A Functional Approach to Learning Molecular Generative Models—0
Minimax Estimation of Linear Functions of Eigenvectors in the Face of Small Eigen-Gaps—0
Minimizing the Maximal Rank—0
Minmax Trend Filtering: Generalizations of Total Variation Denoising via a Local Minmax/Maxmin Formula—0
Adaptive Regularization of Some Inverse Problems in Image Analysis—0
MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results—0
MirrorDiffusion: Stabilizing Diffusion Process in Zero-shot Image Translation by Prompts Redescription and Beyond—0
MissDiff: Training Diffusion Models on Tabular Data with Missing Values—0
Missing data imputation for noisy time-series data and applications in healthcare—0
Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network—0
Mitigating Gradient-based Adversarial Attacks via Denoising and Compression—0
Mitigating Hallucinations in Diffusion Models through Adaptive Attention Modulation—0
Mitigating Long-tail Distribution in Oracle Bone Inscriptions: Dataset, Model, and Benchmark—0
Mitigation of Through-Wall Distortions of Frontal Radar Images using Denoising Autoencoders—0
MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization—0
Towards Robust Time-of-Flight Depth Denoising with Confidence-Aware Diffusion Model—0
Adaptive Regularization in Convex Composite Optimization for Variational Imaging Problems—0
Mixed Gaussian-Impulse Noise Removal from Highly Corrupted Images via Adaptive Local and Nonlocal Statistical Priors—0
Mixed geometry information regularization for image multiplicative denoising—0
Mixed Noise Removal with Pareto Prior—0
Mixed Regression via Approximate Message Passing—0
Mixed-State Quantum Denoising Diffusion Probabilistic Model—0
MixerMDM: Learnable Composition of Human Motion Diffusion Models—0
Mixing-Denoising Generalizable Occupancy Networks—0
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery—0
Mixture of Efficient Diffusion Experts Through Automatic Interval and Sub-Network Selection—0
Mixture of Robust Experts (MoRE):A Robust Denoising Method towards multiple perturbations—0
Towards speech enhancement using a variational U-Net architecture—0
MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling—0
Towards stationary time-vertex signal processing—0
MMO-IG: Multi-Class and Multi-Scale Object Image Generation for Remote Sensing—0
MMSE Estimation for Poisson Noise Removal in Images—0
Adaptive noise imitation for image denoising—0
Mobile Video Diffusion—0
Global Adaptive Filtering Layer for Computer Vision—0
Towards the Automation of Deep Image Prior—0
Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders—0
Model-Based Deep Learning for Reconstruction of Joint k-q Under-sampled High Resolution Diffusion MRI—0
Model-Based Image Signal Processors via Learnable Dictionaries—0
Towards Transferable Speech Emotion Representation: On loss functions for cross-lingual latent representations—0
Towards Transformer-Based Aligned Generation with Self-Coherence Guidance—0
Modeling documents with Generative Adversarial Networks—0
CRISP: Clustering Multi-Vector Representations for Denoising and Pruning—0
Modeling Graph Node Correlations with Neighbor Mixture Models—0
Modeling Pedestrian Intrinsic Uncertainty for Multimodal Stochastic Trajectory Prediction via Energy Plan Denoising—0
Modeling sRGB Camera Noise with Normalizing Flows—0
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion—0
Towards Understanding Cross and Self-Attention in Stable Diffusion for Text-Guided Image Editing—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