Image Super-Resolution Algorithm Based on an Improved Sparse Autoencoder

Huang, Detian; Huang, Weiqin; Yuan, Ze; Lin, Yanming; Zhang, Jian; Zheng, Lixin

doi:10.3390/info9010011

Cited by 5 publications

(4 citation statements)

References 26 publications

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“…Traditional image processing algorithms mainly rely on basic digital image processing techniques. Generally, there are three categories: interpolation-based algorithms [21][22][23], degenerate-model-based algorithms [24][25][26] and learning-based algorithms [27][28][29][30].…”

Section: Traditional Methodsmentioning

confidence: 99%

Super-Resolution Reconstruction of Cytoskeleton Image Based on A-Net Deep Learning Network

Chen

Bai²,

Che³

et al. 2022

Micromachines

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To date, live-cell imaging at the nanometer scale remains challenging. Even though super-resolution microscopy methods have enabled visualization of sub-cellular structures below the optical resolution limit, the spatial resolution is still far from enough for the structural reconstruction of biomolecules in vivo (i.e., ~24 nm thickness of microtubule fiber). In this study, a deep learning network named A-net was developed and shows that the resolution of cytoskeleton images captured by a confocal microscope can be significantly improved by combining the A-net deep learning network with the DWDC algorithm based on a degradation model. Utilizing the DWDC algorithm to construct new datasets and taking advantage of A-net neural network’s features (i.e., considerably fewer layers and relatively small dataset), the noise and flocculent structures which originally interfere with the cellular structure in the raw image are significantly removed, with the spatial resolution improved by a factor of 10. The investigation shows a universal approach for exacting structural details of biomolecules, cells and organs from low-resolution images.

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Section: Traditional Methodsmentioning

confidence: 99%

Super-Resolution Reconstruction of Cytoskeleton Image Based on A-Net Deep Learning Network

Chen

Bai²,

Che³

et al. 2022

Micromachines

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“…Instead, an AE learns an approximation of the input features to identify useful properties of the data. AEs are vital tools for dimensionality reduction (Hinton and Salakhutdinov 2006), feature learning (Vincent et al 2008), image colorization (Zhang et al 2016), higherresolution data generation (Huang et al 2018), and latent space clustering (Yeh et al 2017). Additionally, other versions of AEs such as variational autoencoders (VAEs) (Kingma and Welling 2014) can be used as generative models.…”

Section: Autoencodermentioning

confidence: 99%

A survey of deep network techniques all classifiers can adopt

Ghods

Cook

2020

Data Min Knowl Disc

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Deep neural networks (DNNs) have introduced novel and useful tools to the machine learning community. Other types of classifiers can potentially make use of these tools as well to improve their performance and generality. This paper reviews the current state of the art for deep learning classifier technologies that are being used outside of deep neural networks. Non-neural network classifiers can employ many components found in DNN architectures. In this paper, we review the feature learning, optimization, and regularization methods that form a core of deep network technologies. We then survey non-neural network learning algorithms that make innovative use of these methods to improve classification performance. Because many opportunities and challenges still exist, we discuss directions that can be pursued to expand the area of deep learning for a variety of classification algorithms.

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“…Instead, an AE learns an approximation of the input features to identify useful properties of the data. AEs are vital tools for dimensionality reduction [63], feature learning [64], image colorization [65], higher-resolution data generation [66], and latent space clustering [67]. Additionally, other versions of AEs such as variational autoencoders (VAEs) [68] can be used as generative models.…”

Section: Autoencodermentioning

confidence: 99%

A Survey of Techniques All Classifiers Can Learn from Deep Networks: Models, Optimizations, and Regularization

Ghods,

Cook

2019

Preprint

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Deep neural networks have introduced novel and useful tools to the machine learning community. Other types of classifiers can potentially make use of these tools as well to improve their performance and generality. This paper reviews the current state of the art for deep learning classifier technologies that are being used outside of deep neural networks. Non-network classifiers can employ many components found in deep neural network architectures. In this paper, we review the feature learning, optimization, and regularization methods that form a core of deep network technologies. We then survey non-neural network learning algorithms that make innovative use of these methods to improve classification. Because many opportunities and challenges still exist, we discuss directions that can be pursued to expand the area of deep learning for a variety of classification algorithms.

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Image Super-Resolution Algorithm Based on an Improved Sparse Autoencoder

Cited by 5 publications

References 26 publications

Super-Resolution Reconstruction of Cytoskeleton Image Based on A-Net Deep Learning Network

Super-Resolution Reconstruction of Cytoskeleton Image Based on A-Net Deep Learning Network

A survey of deep network techniques all classifiers can adopt

A Survey of Techniques All Classifiers Can Learn from Deep Networks: Models, Optimizations, and Regularization

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