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J. Imaging
Special Issues
Computational Colour Imaging
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Computational Colour Imaging

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (30 November 2018) | Viewed by 20378

Special Issue Editor

Prof. Ivar Farup

E-Mail Website
Guest Editor
Department of Computer Science, Norwegian University of Science and Technology, Gjøvik, Norway
Interests: colour science; colour image processing; colour geometry; computational methods

Special Issue Information

Dear Colleagues,

With the advent of digital cameras and scanners, mobile devices, various types of displays, and the Web, digital colour images have become ubiquitous. In order to ensure a consistent, convincing, and pleasing reproduction of colour images across the wide variety of currently available technologies, a thorough understanding of the many different aspects of the cross-disciplinary field of colour imaging is needed.

Everything from vision and perception through optics and sensors to display technologies must be taken into account. In order to bind all of this together in a colour imaging chain, various mathematical models and computational algorithms are called for. This includes—but is certainly not limited to—geometric and colorimetric camera calibration, demosaicing, denoising, deblurring, colour balancing, colour conversion, high-dynamic-range image rendering, and colour gamut mapping.

The objective of this Special Issue is to bring together and showcase the various parts of the colour imaging chain where the computational aspects are of particular importance, and to bring forward new and innovative applications and methods. Papers must be original research of novel results or a suitable review of the current state-of-the-art.

Prof. Ivar Farup
Guest Editor

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. Journal of Imaging 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

  • Colour image acquisition
  • Colour image processing
  • Colour image reproduction
  • Colour calibration algorithms
  • Colour gamut mapping
  • HDR image rendering
  • Computational colour constancy
  • Colour appearance models
  • Computational colour science
  • Computational colour vision models
  • Colour space geometry
  • Colour imaging applications

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

12 pages, 1415 KiB  
Article
Optimal Color Lighting for Scanning Images of Flat Panel Display using Simplex Search
by HyungTae Kim, EungJoo Ha, KyungChan Jin and ByungWook Kim
J. Imaging 2018, 4(11), 133; https://doi.org/10.3390/jimaging4110133 - 12 Nov 2018
Cited by 1 | Viewed by 4400
Abstract
A system for inspecting flat panel displays (FPDs) acquires scanning images using multiline charge-coupled device (CCD) cameras and industrial machine vision. Optical filters are currently installed in front of these inspection systems to obtain high-quality images. However, the combination of optical filters required [...] Read more.
A system for inspecting flat panel displays (FPDs) acquires scanning images using multiline charge-coupled device (CCD) cameras and industrial machine vision. Optical filters are currently installed in front of these inspection systems to obtain high-quality images. However, the combination of optical filters required is determined manually and by using empirical methods; this is referred to as passive color control. In this study, active color control is proposed for inspecting FPDs. This inspection scheme requires the scanning of images, which is achieved using a mixed color light source and a mixing algorithm. The light source utilizes high-power light emitting diodes (LEDs) of multiple colors and a communication port to dim their level. Mixed light illuminates an active-matrix organic light-emitting diode (AMOLED) panel after passing through a beam expander and after being shaped into a line beam. The image quality is then evaluated using the Tenenbaum gradient after intensity calibration of the scanning images. The dimming levels are determined using the simplex search method which maximizes the image quality. The color of the light was varied after every scan of an AMOLED panel, and the variation was iterated until the image quality approached a local maximization. The number of scans performed was less than 225, while the number of dimming level combinations was 20484. The proposed method can reduce manual tasks in setting-up inspection machines, and hence is useful for the inspection machines in FPD processes. Full article
(This article belongs to the Special Issue Computational Colour Imaging)
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17 pages, 374 KiB  
Article
Unsupervised Local Binary Pattern Histogram Selection Scores for Color Texture Classification
by Mariam Kalakech, Alice Porebski, Nicolas Vandenbroucke and Denis Hamad
J. Imaging 2018, 4(10), 112; https://doi.org/10.3390/jimaging4100112 - 28 Sep 2018
Cited by 7 | Viewed by 4384
Abstract
These last few years, several supervised scores have been proposed in the literature to select histograms. Applied to color texture classification problems, these scores have improved the accuracy by selecting the most discriminant histograms among a set of available ones computed from a [...] Read more.
These last few years, several supervised scores have been proposed in the literature to select histograms. Applied to color texture classification problems, these scores have improved the accuracy by selecting the most discriminant histograms among a set of available ones computed from a color image. In this paper, two new scores are proposed to select histograms: The adapted Variance score and the adapted Laplacian score. These new scores are computed without considering the class label of the images, contrary to what is done until now. Experiments, achieved on OuTex, USPTex, and BarkTex sets, show that these unsupervised scores give as good results as the supervised ones for LBP histogram selection. Full article
(This article belongs to the Special Issue Computational Colour Imaging)
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20 pages, 9427 KiB  
Article
Stochastic Capsule Endoscopy Image Enhancement
by Ahmed Mohammed, Ivar Farup, Marius Pedersen, Øistein Hovde and Sule Yildirim Yayilgan
J. Imaging 2018, 4(6), 75; https://doi.org/10.3390/jimaging4060075 - 6 Jun 2018
Cited by 15 | Viewed by 6287
Abstract
Capsule endoscopy, which uses a wireless camera to take images of the digestive tract, is emerging as an alternative to traditional colonoscopy. The diagnostic values of these images depend on the quality of revealed underlying tissue surfaces. In this paper, we consider the [...] Read more.
Capsule endoscopy, which uses a wireless camera to take images of the digestive tract, is emerging as an alternative to traditional colonoscopy. The diagnostic values of these images depend on the quality of revealed underlying tissue surfaces. In this paper, we consider the problem of enhancing the visibility of detail and shadowed tissue surfaces for capsule endoscopy images. Using concentric circles at each pixel for random walks combined with stochastic sampling, the proposed method enhances the details of vessel and tissue surfaces. The framework decomposes the image into two detailed layers that contain shadowed tissue surfaces and detail features. The target pixel value is recalculated for the smooth layer using similarity of the target pixel to neighboring pixels by weighting against the total gradient variation and intensity differences. In order to evaluate the diagnostic image quality of the proposed method, we used clinical subjective evaluation with a rank order on selected KID image database and compared it to state-of-the-art enhancement methods. The result showed that the proposed method provides a better result in terms of diagnostic image quality and objective quality contrast metrics and structural similarity index. Full article
(This article belongs to the Special Issue Computational Colour Imaging)
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17 pages, 10914 KiB  
Article
Detection of Orientation-Modulation Embedded Data in Color Printed Natural Images
by Vlado Kitanovski and Marius Pedersen
J. Imaging 2018, 4(4), 56; https://doi.org/10.3390/jimaging4040056 - 4 Apr 2018
Cited by 3 | Viewed by 4674
Abstract
This article addresses methods for detection of orientation-modulation data embedded in color dispersed-dot-halftone images. Several state-of-the-art methods for detection of orientation-embedded data in printed halftone images have been proposed, however they have only been evaluated independently without comparing with each other. We propose [...] Read more.
This article addresses methods for detection of orientation-modulation data embedded in color dispersed-dot-halftone images. Several state-of-the-art methods for detection of orientation-embedded data in printed halftone images have been proposed, however they have only been evaluated independently without comparing with each other. We propose an improved detection method, which is using Principal Component Analysis (PCA) components as oriented-feature extractors, and a probabilistic model for the print-and-scan channel for maximum likelihood detection. The proposed detector and four state-of-the-art detectors are compared with each other in terms of correct detection rate, using a comprehensive testing set of printed natural images captured with three different devices. The proposed detector achieves highest correct detection rate using fewer feature extractors than the other methods, and it is significantly more robust to non-calibrated devices used for capturing the printed images. This is mostly due to the improved PCA-based oriented-feature extractors that are responsive to the embedded orientations and robust and insensitive to the other visual content. Full article
(This article belongs to the Special Issue Computational Colour Imaging)
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