Advanced Machine Learning Techniques for Sensing and Imaging Applications, Volume II

Dear Colleagues,

Recent advances in machine learning, from large-scale optimization to building deep neural networks, are becoming more and more popular in the emerging field of computational sensing and imaging. A wide range of machine learning techniques, including deep learning, sparse and low-rank modeling, manifold learning, unrolled architectures, learning convolutional and tensor models, etc., can be applied to enhance the effectiveness and efficiency of various sensing and imaging systems. By exploiting the underlying image or signal models via a data-driven approach, these advanced machine learning techniques benefit applications from image reconstruction to analysis. 

The goal of this Special Issue is to attract high-quality works containing original research on imaging and sensing related schemes, including novel imaging pipelines, smart sensing design, blind compressed sensing, task-driven imaging and understanding, in which machine learning is the major ingredients. The scope covers research topics ranging from sensing and learning theory, image and system modeling, algorithms, applications in various imaging modalities.

Potential topics include, but are not limited, to the following:

• Novel learning and data-driven imaging systems;
• Model-based blind compressed sensing and reconstruction;
• Deep learning approaches for sensing- or imaging-based applications;
• Sparse and low-rank modeling;
• Dictionary and transform learning;
• Graphical, tensor, manifold, or plug-and-play models;
• Theory or guarantees for learning-based imaging algorithms;
• Analysis of deep architectures or optimization for imaging tasks;
• Computer vision in sensing and imaging systems;
• Learning for imaging applications: MRI, radar imaging, tomography, microscopy, hyperspectral imaging, computational photography, super-resolution, etc.
• Learning-based biomedical or healthcare sensing and processing;
• Novel datasets to enable learning-based imaging tasks, e.g., remote sensing, computational imaging, real image enhancement, virtual reality, etc.

Dr. Bihan Wen
Dr. Zhangyang (Atlas) Wang
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. Micromachines is an international peer-reviewed open access monthly 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 2600 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.

• Advanced Machine Learning Techniques for Sensing and Imaging Applications in Micromachines (12 articles)