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Electronics
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Machine Learning Methods for Solving Optical Imaging Problems
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Machine Learning Methods for Solving Optical Imaging Problems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Optoelectronics".

Deadline for manuscript submissions: closed (15 November 2024) | Viewed by 2158

Special Issue Editors

Dr. Junchao Zhang

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Guest Editor
School of Automation, Central South University, Changsha 410083, China
Interests: image processing; pattern recognition; artificial intelligence; object detection
Special Issues, Collections and Topics in MDPI journals
Dr. Xinglin Hou

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Guest Editor
School of Automotive Engineering, Changzhou Institute of Technology, Changzhou 213032, China
Interests: image processing; optical imaging; industrial inspection
Dr. Jianbo Shao

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Guest Editor
KLA Corporation, Milpitas, CA 95035, USA
Interests: optics; computational imaging; deep learning
Dr. Yu Li

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Guest Editor
National Key Laboratory of Science and Technology on Space Microwave, Xi’an Institute of Space Radio Technology, Xi’an 710000, China
Interests: artificial intelligence; sensing-communication technique; airborne moving target indication (AMTI)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the recent years, consistent efforts have been put into applying machine learning methods to address various problems in optical imaging. Across a growing number of optical imaging techniques, machine learning shows better performance over conventional methods. However, optical imaging spans a broad domain of machine learning methods in various fields, requiring ongoing explorations. Furthermore, the “data-driven” nature of deep learning approaches imposes limitations on their applicability, which calls for further attention. This Special Issue aims to highlight the potentials of machine learning methods across a spectrum of optical imaging techniques, including optical coherence tomography, photoacoustic imaging, optical spectroscopy, super-resolution microscopy and polarization imaging. Additionally, the objective is to investigate potential improvements of deep learning methods by leveraging prior knowledge of optical imaging systems, also known as physics-informed deep learning. Lastly, it aims to explore other emerging deep learning frameworks from the broader academic community, such as vision transformer, to provide additional solutions for optical imaging problems.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  1. Deep learning for optical coherence tomography;
  2. Deep learning for photoacoustic imaging;
  3. Deep learning for optical spectroscopy;
  4. Deep learning for super-resolution microscopy;
  5. Physics-informed deep learning for optical imaging;
  6. Advancing from convolutional neural network by investigating new deep learning architectures for optical imaging;
  7. Advanced imaging technologies;
  8. Computational imaging;
  9. Polarization imaging;
  10. Low-light imaging;
  11. HDR imaging;
  12. Hyperspectral imaging;
  13. Infrared imaging and its applications.

We look forward to receiving your contributions.

Dr. Junchao Zhang
Dr. Xinglin Hou
Dr. Jianbo Shao
Dr. Yu Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • optical imaging
  • machine learning
  • image processing

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Published Papers (2 papers)

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Research

25 pages, 3361 KiB  
Article
Effective Sample Selection and Enhancement of Long Short-Term Dependencies in Signal Detection: HDC-Inception and Hybrid CE Loss
by Yingbin Wang, Weiwei Wang, Yuexin Chen, Xinyu Su, Jinming Chen, Wenhai Yang, Qiyue Li and Chongdi Duan
Electronics 2024, 13(16), 3194; https://doi.org/10.3390/electronics13163194 - 13 Aug 2024
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Abstract
Signal detection and classification tasks, especially in the realm of audio, suffer from difficulties in capturing long short-term dependencies and effectively utilizing samples. Firstly, audio signal detection and classification need to classify audio signals and detect their onset and offset times; therefore, obtaining [...] Read more.
Signal detection and classification tasks, especially in the realm of audio, suffer from difficulties in capturing long short-term dependencies and effectively utilizing samples. Firstly, audio signal detection and classification need to classify audio signals and detect their onset and offset times; therefore, obtaining long short-term dependencies is necessary. The methods based on RNNs have high time complexity and dilated convolution-based methods suffer from the “gridding issue” challenge; thus, the HDC-Inception module is proposed to efficiently extract long short-term dependencies. Combining the advantages of the Inception module and a hybrid dilated convolution (HDC) framework, the HDC-Inception module can both alleviate the “gridding issue” and obtain long short-term dependencies. Secondly, datasets have large numbers of silent segments and too many samples for some signal types, which are redundant and less difficult to detect, and, therefore, should not be overly prioritized. Thus, selecting effective samples and guiding the training based on them is of great importance. Inspired by support vector machine (SVM), combining soft margin SVM and cross-entropy loss (CE loss), the soft margin CE loss is proposed. Soft margin CE loss can adaptively select support vectors (effective samples) in datasets and guide training based on the selected samples. To utilize datasets more sufficiently, a hybrid CE loss is proposed. Using the benefits of soft margin CE loss and CE loss, hybrid CE loss guides the training with all samples and gives weight to support vectors. Soft margin CE loss and hybrid CE loss can be extended to most classification tasks and offer a wide range of applications and great potential. Full article
(This article belongs to the Special Issue Machine Learning Methods for Solving Optical Imaging Problems)
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17 pages, 13327 KiB  
Article
Fusion of Infrared and Visible Light Images Based on Improved Adaptive Dual-Channel Pulse Coupled Neural Network
by Bin Feng, Chengbo Ai and Haofei Zhang
Electronics 2024, 13(12), 2337; https://doi.org/10.3390/electronics13122337 - 14 Jun 2024
Cited by 2 | Viewed by 849
Abstract
The pulse-coupled neural network (PCNN), due to its effectiveness in simulating the mammalian visual system to perceive and understand visual information, has been widely applied in the fields of image segmentation and image fusion. To address the issues of low contrast and the [...] Read more.
The pulse-coupled neural network (PCNN), due to its effectiveness in simulating the mammalian visual system to perceive and understand visual information, has been widely applied in the fields of image segmentation and image fusion. To address the issues of low contrast and the loss of detail information in infrared and visible light image fusion, this paper proposes a novel image fusion method based on an improved adaptive dual-channel PCNN model in the non-subsampled shearlet transform (NSST) domain. Firstly, NSST is used to decompose the infrared and visible light images into a series of high-pass sub-bands and a low-pass sub-band, respectively. Next, the PCNN models are stimulated using the weighted sum of the eight-neighborhood Laplacian of the high-pass sub-bands and the energy activity of the low-pass sub-band. The high-pass sub-bands are fused using local structural information as the basis for the linking strength for the PCNN, while the low-pass sub-band is fused using a linking strength based on multiscale morphological gradients. Finally, the fused high-pass and low-pass sub-bands are reconstructed to obtain the fused image. Comparative experiments demonstrate that, subjectively, this method effectively enhances the contrast of scenes and targets while preserving the detail information of the source images. Compared to the best mean values of the objective evaluation metrics of the compared methods, the proposed method shows improvements of 2.35%, 3.49%, and 11.60% in information entropy, mutual information, and standard deviation, respectively. Full article
(This article belongs to the Special Issue Machine Learning Methods for Solving Optical Imaging Problems)
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