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 3876–3900 of 7282 papers

TitleStatusHype
Towards Prompt-robust Face Privacy Protection via Adversarial Decoupling Augmentation Framework—0
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
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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