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J. Imaging
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Advances in Color Imaging
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Advances in Color Imaging

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Color, Multi-spectral, and Hyperspectral Imaging".

Deadline for manuscript submissions: closed (1 April 2022) | Viewed by 16908

Special Issue Editors

Dr. Vien Cheung

E-Mail Website
Guest Editor
School of Design, University of Leeds, Leeds, Leeds LS2 9JT, UK
Interests: color vision; color science; cross-media reproduction; color management; spectral imaging; color design
Special Issues, Collections and Topics in MDPI journals
Dr. Jean-Baptiste Thomas

E-Mail Website
Guest Editor
Department of Computer Science, Norwegian University of Science and Technology, 2806 Gjøvik, Norway
Interests: spectral imaging; material appearance; colour imaging; computational appearance
Special Issues, Collections and Topics in MDPI journals
Dr. Peter Rhodes

E-Mail Website
Guest Editor
School of Design, University of Leeds, Leeds LS2 9JT, UK
Interests: image capture and reproduction; color specification, management, and visualization; appearance measurement
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Color imaging—spanning the capture, encoding, compression, and reproduction of color images—is an important part of today’s digital world. Decades of color imaging research have provided a solid foundation to enable a wide range of applications: appearance measurement, artwork archiving and restoration, computer gaming, security and camouflage imaging, multispectral or medical diagnosis, etc. Nevertheless, new technologies such as HDR imaging, wide color gamut, 3D printing, illumination, and immersive realities are rapidly developing to further enhance user experience. How do we make the most of comprehensive color imaging research understanding using these new technologies? The theme of this Special Issue of the Journal of Imaging is advances in color imaging. In addition to original research papers with novel findings and review articles describing the current state of the art, papers discussing outstanding issues that need scientific research and future research directions are invited. Transdisciplinary studies using or developing color imaging are also welcome.

Dr. Vien Cheung
Dr. Jean-Baptiste Thomas
Dr. Peter Rhodes
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. 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

  • Color vision and perception
  • Computational color and color coding
  • Color and material appearance modeling
  • Color in illumination and lighting
  • Image quality and visualization
  • Spectral imaging
  • Augmented reality (AR), virtual reality (VR), mixed reality (MR)

Published Papers (6 papers)

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Research

18 pages, 5861 KiB  
Article
Evaluating the Influence of ipRGCs on Color Discrimination
by Masaya Ohtsu, Akihiro Kurata, Keita Hirai, Midori Tanaka and Takahiko Horiuchi
J. Imaging 2022, 8(6), 154; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8060154 - 28 May 2022
Viewed by 1859
Abstract
To investigate the influence of intrinsically photosensitive retinal ganglion cells (ipRGCs) on color discrimination, it is necessary to create two metameric light stimuli (metameric ipRGC stimuli) with the same amount of cone and rod stimulation, but different amounts of ipRGC stimulation. However, since [...] Read more.
To investigate the influence of intrinsically photosensitive retinal ganglion cells (ipRGCs) on color discrimination, it is necessary to create two metameric light stimuli (metameric ipRGC stimuli) with the same amount of cone and rod stimulation, but different amounts of ipRGC stimulation. However, since the spectral sensitivity functions of cones and rods overlap with those of ipRGCs in a wavelength band, it has been difficult to independently control the amount of stimulation of ipRGCs only. In this study, we first propose a method for calculating metameric ipRGC stimulation based on the orthogonal basis functions of human photoreceptor cells. Then, we clarify the controllable range of metameric ipRGC stimulation within a color gamut. Finally, to investigate the color discrimination by metameric ipRGC stimuli, we conduct subjective evaluation experiments on 24 chromaticity coordinates using a multispectral projector. The results reveal a correlation between differences in the amount of ipRGC stimulation and differences in color appearance, indicating that ipRGCs may influence color discrimination. Full article
(This article belongs to the Special Issue Advances in Color Imaging)
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21 pages, 5792 KiB  
Article
Salient Object Detection by LTP Texture Characterization on Opposing Color Pairs under SLICO Superpixel Constraint
by Didier Ndayikengurukiye and Max Mignotte
J. Imaging 2022, 8(4), 110; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8040110 - 13 Apr 2022
Cited by 5 | Viewed by 3136
Abstract
The effortless detection of salient objects by humans has been the subject of research in several fields, including computer vision, as it has many applications. However, salient object detection remains a challenge for many computer models dealing with color and textured images. Most [...] Read more.
The effortless detection of salient objects by humans has been the subject of research in several fields, including computer vision, as it has many applications. However, salient object detection remains a challenge for many computer models dealing with color and textured images. Most of them process color and texture separately and therefore implicitly consider them as independent features which is not the case in reality. Herein, we propose a novel and efficient strategy, through a simple model, almost without internal parameters, which generates a robust saliency map for a natural image. This strategy consists of integrating color information into local textural patterns to characterize a color micro-texture. It is the simple, yet powerful LTP (Local Ternary Patterns) texture descriptor applied to opposing color pairs of a color space that allows us to achieve this end. Each color micro-texture is represented by a vector whose components are from a superpixel obtained by the SLICO (Simple Linear Iterative Clustering with zero parameter) algorithm, which is simple, fast and exhibits state-of-the-art boundary adherence. The degree of dissimilarity between each pair of color micro-textures is computed by the FastMap method, a fast version of MDS (Multi-dimensional Scaling) that considers the color micro-textures’ non-linearity while preserving their distances. These degrees of dissimilarity give us an intermediate saliency map for each RGB (Red–Green–Blue), HSL (Hue–Saturation–Luminance), LUV (L for luminance, U and V represent chromaticity values) and CMY (Cyan–Magenta–Yellow) color space. The final saliency map is their combination to take advantage of the strength of each of them. The MAE (Mean Absolute Error), MSE (Mean Squared Error) and Fβ measures of our saliency maps, on the five most used datasets show that our model outperformed several state-of-the-art models. Being simple and efficient, our model could be combined with classic models using color contrast for a better performance. Full article
(This article belongs to the Special Issue Advances in Color Imaging)
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16 pages, 98627 KiB  
Article
Perceptually Optimal Color Representation of Fully Polarimetric SAR Imagery
by Georgia Koukiou
J. Imaging 2022, 8(3), 67; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8030067 - 07 Mar 2022
Cited by 2 | Viewed by 2684
Abstract
The four bands of fully polarimetric SAR data convey scattering characteristics of the Earth’s background, but perceptually are not very easy for an observer to use. In this work, the four different channels of fully polarimetric SAR images, namely HH, HV, VH, and [...] Read more.
The four bands of fully polarimetric SAR data convey scattering characteristics of the Earth’s background, but perceptually are not very easy for an observer to use. In this work, the four different channels of fully polarimetric SAR images, namely HH, HV, VH, and VV, are combined so that a color image of the Earth’s background is derived that is perceptually excellent for the human eye and at the same time provides accurate information regarding the scattering mechanisms in each pixel. Most of the elementary scattering mechanisms are related to specific color and land cover types. The innovative nature of the proposed approach is due to the two different consecutive coloring procedures. The first one is a fusion procedure that moves all the information contained in the four polarimetric channels into three derived RGB bands. This is achieved by means of Cholesky decomposition and brings to the RGB output the correlation properties of a natural color image. The second procedure moves the color information of the RGB image to the CIELab color space, which is perceptually uniform. The color information is then evenly distributed by means of color equalization in the CIELab color space. After that, the inverse procedure to obtain the final RGB image is performed. These two procedures bring the PolSAR information regarding the scattering mechanisms on the Earth’s surface onto a meaningful color image, the appearance of which is close to Google Earth maps. Simultaneously, they give better color correspondence to various land cover types compared with existing SAR color representation methods. Full article
(This article belongs to the Special Issue Advances in Color Imaging)
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19 pages, 85081 KiB  
Article
Hue-Preserving Saturation Improvement in RGB Color Cube
by Kohei Inoue, Minyao Jiang and Kenji Hara
J. Imaging 2021, 7(8), 150; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7080150 - 18 Aug 2021
Cited by 8 | Viewed by 3353
Abstract
This paper proposes a method for improving saturation in the context of hue-preserving color image enhancement. The proposed method handles colors in an RGB color space, which has the form of a cube, and enhances the contrast of a given image by histogram [...] Read more.
This paper proposes a method for improving saturation in the context of hue-preserving color image enhancement. The proposed method handles colors in an RGB color space, which has the form of a cube, and enhances the contrast of a given image by histogram manipulation, such as histogram equalization and histogram specification, of the intensity image. Then, the color corresponding to a target intensity is determined in a hue-preserving manner, where a gamut problem should be taken into account. We first project any color onto a surface in the RGB color space, which bisects the RGB color cube, to increase the saturation without a gamut problem. Then, we adjust the intensity of the saturation-enhanced color to the target intensity given by the histogram manipulation. The experimental results demonstrate that the proposed method achieves higher saturation than that given by related methods for hue-preserving color image enhancement. Full article
(This article belongs to the Special Issue Advances in Color Imaging)
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12 pages, 3676 KiB  
Article
Yellow Pigment Powders Based on Lead and Antimony: Particle Size and Colour Hue
by Giuseppe Capobianco, Giorgia Agresti, Giuseppe Bonifazi, Silvia Serranti and Claudia Pelosi
J. Imaging 2021, 7(8), 127; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7080127 - 30 Jul 2021
Cited by 4 | Viewed by 1897
Abstract
This paper reports the results of particle size analysis and colour measurements concerning yellow powders, synthesised in our laboratories according to ancient recipes aiming at producing pigments for paintings, ceramics, and glasses. These pigments are based on lead and antimony as chemical elements, [...] Read more.
This paper reports the results of particle size analysis and colour measurements concerning yellow powders, synthesised in our laboratories according to ancient recipes aiming at producing pigments for paintings, ceramics, and glasses. These pigments are based on lead and antimony as chemical elements, that, combined in different proportions and fired at different temperatures, times, and with various additives, gave materials of yellow colours, changing in hues and particle size. Artificial yellow pigments, based on lead and antimony, have been widely studied, but no specific investigation on particle size distribution and its correlation to colour hue has been performed before. In order to evaluate the particle size distribution, segmentation of sample data has been performed using the MATLAB software environment. The extracted parameters were examined by principal component analysis (PCA) in order to detect differences and analogies between samples on the base of those parameters. Principal component analysis was also applied to colour data acquired by a reflectance spectrophotometer in the visible range according to the CIELAB colour space. Within the two examined groups, i.e., yellows containing NaCl and those containing K-tartrate, differences have been found between samples and also between different areas of the same powder indicating the inhomogeneity of the synthesised pigments. On the other hand, colour data showed homogeneity within each yellow sample and clear differences between the different powders. The comparison of results demonstrates the potentiality of the particle segmentation and analysis in the study of morphology and distribution of pigment powders produced artificially, allowing the characterisation of the lead and antimony-based pigments through micro-image analysis and colour measurements combined with a multivariate approach. Full article
(This article belongs to the Special Issue Advances in Color Imaging)
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17 pages, 7142 KiB  
Article
Psychophysical Determination of the Relevant Colours That Describe the Colour Palette of Paintings
by Juan Luis Nieves, Juan Ojeda, Luis Gómez-Robledo and Javier Romero
J. Imaging 2021, 7(4), 72; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7040072 - 14 Apr 2021
Cited by 5 | Viewed by 2210
Abstract
In an early study, the so-called “relevant colour” in a painting was heuristically introduced as a term to describe the number of colours that would stand out for an observer when just glancing at a painting. The purpose of this study is to [...] Read more.
In an early study, the so-called “relevant colour” in a painting was heuristically introduced as a term to describe the number of colours that would stand out for an observer when just glancing at a painting. The purpose of this study is to analyse how observers determine the relevant colours by describing observers’ subjective impressions of the most representative colours in paintings and to provide a psychophysical backing for a related computational model we proposed in a previous work. This subjective impression is elicited by an efficient and optimal processing of the most representative colour instances in painting images. Our results suggest an average number of 21 subjective colours. This number is in close agreement with the computational number of relevant colours previously obtained and allows a reliable segmentation of colour images using a small number of colours without introducing any colour categorization. In addition, our results are in good agreement with the directions of colour preferences derived from an independent component analysis. We show that independent component analysis of the painting images yields directions of colour preference aligned with the relevant colours of these images. Following on from this analysis, the results suggest that hue colour components are efficiently distributed throughout a discrete number of directions and could be relevant instances to a priori describe the most representative colours that make up the colour palette of paintings. Full article
(This article belongs to the Special Issue Advances in Color Imaging)
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