Special Issue "Machine Learning and Compressed Sensing in Image Reconstruction"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 15 June 2019

Special Issue Editors

Guest Editor
Prof. Dr. Markus Haltmeier

Department of Mathematics, University of Innsbruck, Innsbruck, Austria
Website | E-Mail
Interests: inverse problems; computed tomography; computational imaging; applied mathematics; machine learning
Guest Editor
Prof. Dr. Linh. V. Nguyen

Department of Mathematics, University of Idaho, USA
Website | E-Mail
Interests: inverse problems; computed tomography; computational imaging; applied mathematics; machine learning

Special Issue Information

Dear Colleagues,

The development of fast and accurate reconstruction algorithms plays a central role in modern imaging systems. Examples include x-ray tomography, ultrasound imaging, photoacoustic imaging, super-resolution imaging, and magnetic resonance imaging. Compressed sensing and machine learning are successful tools for various imaging applications. Compressed sensing techniques allow to significantly reduce the amount of data to be acquired and thereby accelerates data acquisition, reduces motion artefacts, and lowers radiation exposure. In compressed sensing, iterative algorithms based on prior information have been applied for image reconstruction. Such algorithms can be time-consuming as the forward and adjoint problems have to be computed repeatedly. Recently, a new class of algorithms based on machine learning, especially deep learning, for compressed sensing and other image reconstruction tasks appeared. With deep learning, image reconstruction can be performed efficiently using artificial neural networks, whose weights are based on training data. While still in their infancy, these techniques already show astonishing performance. This Special Issue focuses on the latest research and development of compressed sensing and machine learning for image reconstruction. Papers on the design of new reconstruction algorithms and new compressed sensing and machine learning applications are welcome. In addition, contributions on the theoretical analysis and understanding of compressed sensing and machine learning in image reconstruction are welcome.

Prof. Dr. Markus Haltmeier
Prof. Dr. Linh. V. Nguyen
Guest Editors

Manuscript Submission Information

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Keywords

  • computed tomography
  • compressed sensing
  • sampling theory
  • iterative algorithms
  • photoacoustic tomography
  • magnetic resonance imaging
  • parameter identification
  • regularization methods
  • sparse recovery
  • neural networks
  • deep learning image reconstruction
  • data consistency

Published Papers (3 papers)

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Research

Open AccessArticle Fast Cylinder Shape Matching Using Random Sample Consensus in Large Scale Point Cloud
Appl. Sci. 2019, 9(5), 974; https://doi.org/10.3390/app9050974
Received: 31 January 2019 / Revised: 3 March 2019 / Accepted: 4 March 2019 / Published: 7 March 2019
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Abstract
In this paper, an algorithm is proposed that can perform cylinder type matching faster than the existing method in point clouds that represent space. The existing matching method uses Hough transform and completes the matching through preprocessing such as noise filtering, normal estimation, [...] Read more.
In this paper, an algorithm is proposed that can perform cylinder type matching faster than the existing method in point clouds that represent space. The existing matching method uses Hough transform and completes the matching through preprocessing such as noise filtering, normal estimation, and segmentation. The proposed method completes the matching through the methodology of random sample consensus (RANSAC) and principal component analysis (PCA). Cylindrical pipe estimation is based on two mathematical models that compute the parameters and combine the results to predict spheres and lines. RANSAC fitting computes the center and radius of the sphere, which can be the radius of the cylinder axis and finds straight and curved areas through PCA. This allows fast matching without normal estimation and segmentation. Linear and curved regions are distinguished by a discriminant using eigenvalues. The linear region is the sum of the vectors of linear candidates, and the curved region is matched by a Catmull–Rom spline. The proposed method is expected to improve the work efficiency of the reverse design by matching linear and curved cylinder estimation without vertical/horizontal constraint and segmentation. It is also more than 10 times faster while maintaining the accuracy of the conventional method. Full article
(This article belongs to the Special Issue Machine Learning and Compressed Sensing in Image Reconstruction)
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Open AccessArticle Product Innovation Design Based on Deep Learning and Kansei Engineering
Appl. Sci. 2018, 8(12), 2397; https://doi.org/10.3390/app8122397
Received: 17 October 2018 / Revised: 12 November 2018 / Accepted: 20 November 2018 / Published: 26 November 2018
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Abstract
Creative product design is becoming critical to the success of many enterprises. However, the conventional product innovation process is hindered by two major challenges: the difficulty to capture users’ preferences and the lack of intuitive approaches to visually inspire the designer, which is [...] Read more.
Creative product design is becoming critical to the success of many enterprises. However, the conventional product innovation process is hindered by two major challenges: the difficulty to capture users’ preferences and the lack of intuitive approaches to visually inspire the designer, which is especially true in fashion design and form design of many other types of products. In this paper, we propose to combine Kansei engineering and the deep learning for product innovation (KENPI) framework, which can transfer color, pattern, etc. of a style image in real time to a product’s shape automatically. To capture user preferences, we combine Kansei engineering with back-propagation neural networks to establish a mapping model between product properties and styles. To address the inspiration issue in product innovation, the convolutional neural network-based neural style transfer is adopted to reconstruct and merge color and pattern features of the style image, which are then migrated to the target product. The generated new product image can not only preserve the shape of the target product but also have the features of the style image. The Kansei analysis shows that the semantics of the new product have been enhanced on the basis of the target product, which means that the new product design can better meet the needs of users. Finally, implementation of this proposed method is demonstrated in detail through a case study of female coat design. Full article
(This article belongs to the Special Issue Machine Learning and Compressed Sensing in Image Reconstruction)
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Open AccessArticle Infrared Image Super-Resolution Reconstruction Based on Quaternion Fractional Order Total Variation with Lp Quasinorm
Appl. Sci. 2018, 8(10), 1864; https://doi.org/10.3390/app8101864
Received: 3 September 2018 / Revised: 1 October 2018 / Accepted: 2 October 2018 / Published: 10 October 2018
Cited by 1 | PDF Full-text (10500 KB) | HTML Full-text | XML Full-text
Abstract
Owing to the limitations of the imaging principle as well as the properties of imaging systems, infrared images often have some drawbacks, including low resolution, a lack of detail, and indistinct edges. Therefore, it is essential to improve infrared image quality. Considering the [...] Read more.
Owing to the limitations of the imaging principle as well as the properties of imaging systems, infrared images often have some drawbacks, including low resolution, a lack of detail, and indistinct edges. Therefore, it is essential to improve infrared image quality. Considering the information of neighbors, a description of sparse edges, and by avoiding staircase artifacts, a new super-resolution reconstruction (SRR) method is proposed for infrared images, which is based on fractional order total variation (FTV) with quaternion total variation and the L p quasinorm. Our proposed method improves the sparsity exploitation of FTV, and efficiently preserves image structures. Furthermore, we adopt the plug-and-play alternating direction method of multipliers (ADMM) and the fast Fourier transform (FFT) theory for the proposed method to improve the efficiency and robustness of our algorithm; in addition, an accelerated step is adopted. Our experimental results show that the proposed method leads to excellent performances in terms of an objective evaluation and the subjective visual effect. Full article
(This article belongs to the Special Issue Machine Learning and Compressed Sensing in Image Reconstruction)
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