Applications of Computation in Multispectral and Hyperspectral Imaging Systems

A special issue of Computation (ISSN 2079-3197).

Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 4887

Special Issue Editors


E-Mail Website
Guest Editor
Department of Optics, University of Granada, 18071 Granada, Spain
Interests: vision; color vision; color vision deficiencies; multispectral imaging; hyperspectral imaging; computation color imaging; spectral imaging; HDR imaging; color and spectral image processing; atmospheric optics; teaching optics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Optics, University of Granada, 18071 Granada, Spain
Interests: multispectral and hyperspectral imaging; color image processing; saliency prediction; spectral imaging of artwork
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Optics, University of Granada, 18071 Granada, Spain
Interests: spectral and color imaging; computational color; color vision; high dynamic range imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Technological advances in image sensors and spectral filtering have allowed the proliferation of multispectral and hyperspectral imaging systems for image capture in a wide range of applications, such as medicine, remote sensing, biology, cosmetics, quality control, surveillance, food industry, and art observation, just to name a few.

The development of new system architectures and devices, as well as the increase of computational power, have put spectral imaging to a main stream of scientific and industrial interest. These imaging systems enable us to retrieve image data far beyond the capabilities of the human visual system. A multispectral imaging system usually combines two to eight spectral imaging bands of relatively wide bandwidth into a single optical system, whereas a hyperspectral imaging system combines many narrow spectral bands to get the spectrum at each pixel location. Therefore, hyperspectral imaging systems integrate spectroscopic and imaging techniques to enable direct identification of different components and their spatial distribution in the tested sample.

Moreover, this capability of sensing a big amount of data pixel-wise, also makes spectral images become perfect candidates for being used in deep learning techniques.

The impact of computational methods in multispectral and hyperspectral imaging and processing is rapidly increasing. Nevertheless, the lacking of publicly available data for some applications (especially if we compare with the amount of data existing for conventional color imaging) and the huge amount of information that a spectral image can contain are acting as deterrents for the extensive use of recently developed prediction and classification algorithms like deep-learning-based methods.  

The present Special Issue aims to present the recent advances in the development and application of computational methods in multispectral and hyperspectral imaging to further explore its potential in various applications, according but not limited to the list of keywords below. In addition to original research papers with novel research or suitable review articles describing the current state-of-the-art and the future perspectives are invited.

Prof. Dr. Javier Hernández-Andrés
Prof. Dr. Eva M. Valero Benito
Dr. Miguel Angel Martínez Domingo
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. Computation 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 1800 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

  • registration and data/sensor/information fusion
  • segmentation
  • band selection, dimensionality reduction, data compression
  • feature extraction and pattern recognition
  • image classification, hyperspectral unmixing, endmember finding
  • saliency
  • color quality assessment (image quality assessment)
  • spectral estimation
  • spectral databases
  • denoising, dehazing
  • compressive sensing and sensor design
  • data visualization

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

23 pages, 32054 KiB  
Article
Configuration and Registration of Multi-Camera Spectral Image Database of Icon Paintings
by Arash Mirhashemi
Computation 2019, 7(3), 47; https://doi.org/10.3390/computation7030047 - 29 Aug 2019
Cited by 2 | Viewed by 4209
Abstract
At the cost of added complexity and time, hyperspectral imaging provides a more accurate measure of the scene’s irradiance compared to an RGB camera. Several camera designs with more than three channels have been proposed to improve the accuracy. The accuracy is often [...] Read more.
At the cost of added complexity and time, hyperspectral imaging provides a more accurate measure of the scene’s irradiance compared to an RGB camera. Several camera designs with more than three channels have been proposed to improve the accuracy. The accuracy is often evaluated based on the estimation quality of the spectral data. Currently, such evaluations are carried out with either simulated data or color charts to relax the spatial registration requirement between the images. To overcome this limitation, this article presents an accurately registered image database of six icon paintings captured with five cameras with different number of channels, ranging from three (RGB) to more than a hundred (hyperspectral camera). Icons are challenging topics because they have complex surfaces that reflect light specularly with a high dynamic range. Two contributions are proposed to tackle this challenge. First, an imaging configuration is carefully arranged to control the specular reflection, confine the dynamic range, and provide a consistent signal-to-noise ratio for all the camera channels. Second, a multi-camera, feature-based registration method is proposed with an iterative outlier removal phase that improves the convergence and the accuracy of the process. The method was tested against three other approaches with different features or registration models. Full article
Show Figures

Figure 1

Back to TopTop