Significados de denoising en inglés

We demonstrate that DCA denoising improves a diverse set of typical scRNA-seq data analyses using simulated and real datasets.

Principal component analysis is used as a denoising technique by including only low-order components to approximate the EPRI projection data.

SCALE substantially outperforms the other tools in all aspects of scATAC-seq data analysis, including visualization, clustering, and denoising and imputation.

We found that the curvelet-denoising filter followed by FIRE, a process we call CT-FIRE, outperforms the other algorithms under investigation.

The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance.

Comprehensive experiments on widely used benchmarks demonstrate that the proposed method significantly surpasses existing methods on the task of real-world image denoising.

However, noise due to amplification and dropout may obstruct analyses, so scalable denoising methods for increasingly large but sparse scRNA-seq data are needed.

We propose a Deep Boosting Framework (DBF) for real-world image denoising by integrating the deep learning technique into the boosting algorithm.

The capabilities of the proposed method are first validated on several representative simulation tasks including non-blind and blind Gaussian denoising and JPEG image deblocking.

Moreover, we conduct comprehensive analysis on the domain shift issue for real-world denoising and propose an effective one-shot domain transfer scheme to address this issue.

This study aimed to prospectively validate VI-RADS using a next-generation MRI scanner and to investigate the usefulness of denoising deep learning reconstruction (dDLR).

We demonstrate that DCA denoising improves a diverse set of typical scRNA-seq data analyses using simulated and real datasets.

Principal component analysis is used as a denoising technique by including only low-order components to approximate the EPRI projection data.

SCALE substantially outperforms the other tools in all aspects of scATAC-seq data analysis, including visualization, clustering, and denoising and imputation.

We found that the curvelet-denoising filter followed by FIRE, a process we call CT-FIRE, outperforms the other algorithms under investigation.

The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance.