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 2551–2575 of 7282 papers

TitleStatusHype
Fast and Provable ADMM for Learning with Generative Priors—0
Fast, Accurate Manifold Denoising by Tunneling Riemannian Optimization—0
Attention-based network for low-light image enhancement—0
Fast and Accurate Poisson Denoising with Optimized Nonlinear Diffusion—0
A High-Quality Denoising Dataset for Smartphone Cameras—0
Attentional Graph Neural Network Is All You Need for Robust Massive Network Localization—0
Fast 3D Point Cloud Denoising via Bipartite Graph Approximation & Total Variation—0
Fast and Flexible Image Blind Denoising via Competition of Experts—0
Data-Driven Estimation of the False Positive Rate of the Bayes Binary Classifier via Soft Labels—0
AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodies—0
Data Discovery Using Lossless Compression-Based Sparse Representation—0
Data denoising with self consistency, variance maximization, and the Kantorovich dominance—0
Attacks and Defenses for Generative Diffusion Models: A Comprehensive Survey—0
2.5D Deep Learning for CT Image Reconstruction using a Multi-GPU implementation—0
Fast and High-Quality Image Denoising via Malleable Convolutions—0
A Truncated EM Approach for Spike-and-Slab Sparse Coding—0
A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment—0
Data Augmentation with Diffusion Models for Colon Polyp Localization on the Low Data Regime: How much real data is enough?—0
Data Augmentation via Diffusion Model to Enhance AI Fairness—0
A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising—0
A Graph Completion Method that Jointly Predicts Geometry and Topology Enables Effective Molecule Assembly—0
Data augmentation versus noise compensation for x- vector speaker recognition systems in noisy environments—0
Data Augmentation for Diverse Voice Conversion in Noisy Environments—0
A Training-Free Plug-and-Play Watermark Framework for Stable Diffusion—0
FaR: Enhancing Multi-Concept Text-to-Image Diffusion via Concept Fusion and Localized Refinement—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