Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.9
2.7
Journals
J. Imaging
Special Issues
Color in Image Processing and Computer Vision
share announcement

Color in Image Processing and Computer Vision

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 3886

Special Issue Editors

Prof. Dr. Alain Tremeau

E-Mail Website
Guest Editor
Laboratoire Hubert Curien UMR 5516, Université Jean Monnet, 42023 Saint-Étienne, France
Interests: human body pose estimation; human body tracking and trajectories estimation; environmental remote sensing; computer vision; color imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Buzzelli

E-Mail Website
Guest Editor
Department of Informatics, Systems and Communication, University of Milan-Bicocca, 20126 Milano, Italy
Interests: signal/image/video processing and understanding; color imaging; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shoji Tominaga

E-Mail Website
Guest Editor
1. Department of Computer Science, Norwegian University of Science and Technology, 2815 Gjøvik, Norway
2. Faulty of Business and Informatics, Nagano University, Nagano 386-0032, Japan
Interests: multispectral imaging; material appearance; HDR image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational Color Imaging involves the analysis, processing and understanding of color images by computers. It plays a pivotal role in various fields, ranging from environmental monitoring to medical diagnostics, and offers enhanced capabilities for analyzing and understanding visual data. It also plays a pivotal role in Computer Vision and Object recognition.

This Special Issue aims to showcase research that bridges the gap between advanced color image processing techniques and the practical challenges of computer vision, highlighting innovative approaches and AI-driven applications. We encourage submissions that not only present novel research, but also demonstrate the practical implications and potential applications of color features in image processing and computer vision.

We invite contributions that explore novel methodologies, algorithms, and applications in areas including, but not limited to, the following:

  • Color image enhancement and restoration
  • Color perception and color spaces
  • Color and material appearance and reproduction
  • Color image segmentation and classification
  • Multispectral and hyperspectral imaging
  • Computational photography and image rendering
  • Color image quality assessment
  • Color-based computer vision and image understanding
  • Machine learning for color image analysis
  • Color in computer graphics and computer vision
  • Color in cultural heritage and art preservation
  • Advances in color sensor technology and light detection
  • High dynamic range and color processing in 3D Imaging
  • Color imaging applications in scientific research
  • Color image applications in fields such as medical imaging, remote sensing, and more

We also welcome extended papers from the Computational Color Imaging Workshop (CCIW), to be held in September 2024 in Milano, Italy.

Created in 2007, the Computational Color Imaging Workshop (CCIW) is a premier international forum on color images and advanced types of images (spectral, 3D, etc.), including their acquisition, processing, rendering, quality assessment, analysis, and reproduction. The workshop also addresses color vision and material appearance. Applications of color imaging in many fields are included, such as computer vision, health and natural sciences, art and design, video and display, printing and manufacturing, remote sensing and natural sciences.

Prof. Dr. Alain Tremeau
Dr. Marco Buzzelli
Prof. Dr. Shoji Tominaga
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 sensors and light detection
  • color in image processing
  • color in computer vision and objects recognition
  • machine learning and color image analysis
  • multispectral imaging in the VIS and IR domains

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 3640 KiB  
Article
Evaluation of Color Difference Models for Wide Color Gamut and High Dynamic Range
by Olga Basova, Sergey Gladilin, Vladislav Kokhan, Mikhalina Kharkevich, Anastasia Sarycheva, Ivan Konovalenko, Mikhail Chobanu and Ilya Nikolaev
J. Imaging 2024, 10(12), 317; https://doi.org/10.3390/jimaging10120317 - 10 Dec 2024
Viewed by 283
Abstract
Color difference models (CDMs) are essential for accurate color reproduction in image processing. While CDMs aim to reflect perceived color differences (CDs) from psychophysical data, they remain largely untested in wide color gamut (WCG) and high dynamic range (HDR) contexts, which are underrepresented [...] Read more.
Color difference models (CDMs) are essential for accurate color reproduction in image processing. While CDMs aim to reflect perceived color differences (CDs) from psychophysical data, they remain largely untested in wide color gamut (WCG) and high dynamic range (HDR) contexts, which are underrepresented in current datasets. This gap highlights the need to validate CDMs across WCG and HDR. Moreover, the non-geodesic structure of perceptual color space necessitates datasets covering CDs of various magnitudes, while most existing datasets emphasize only small and threshold CDs. To address this, we collected a new dataset encompassing a broad range of CDs in WCG and HDR contexts and developed a novel CDM fitted to these data. Benchmarking various CDMs using STRESS and significant error fractions on both new and established datasets reveals that CAM16-UCS with power correction is the most versatile model, delivering strong average performance across WCG colors up to 1611 cd/m2. However, even the best CDM fails to achieve the desired accuracy limits and yields significant errors. CAM16-UCS, though promising, requires further refinement, particularly in its power correction component to better capture the non-geodesic structure of perceptual color space. Full article
(This article belongs to the Special Issue Color in Image Processing and Computer Vision)
►▼ Show Figures

Figure 1

13 pages, 6949 KiB  
Article
Impact of Display Sub-Pixel Arrays on Perceived Gloss and Transparency
by Midori Tanaka, Kosei Aketagawa and Takahiko Horiuchi
J. Imaging 2024, 10(9), 221; https://doi.org/10.3390/jimaging10090221 - 8 Sep 2024
Viewed by 1081
Abstract
In recent years, improvements in display image quality have made it easier to perceive rich object information, such as gloss and transparency, from images, known as shitsukan. Do the different display specifications in the world affect their appearance? Clarifying the effects of differences [...] Read more.
In recent years, improvements in display image quality have made it easier to perceive rich object information, such as gloss and transparency, from images, known as shitsukan. Do the different display specifications in the world affect their appearance? Clarifying the effects of differences in pixel structure on shitsukan perception is necessary to realize shitsukan management for displays with different hardware structures, which has not been fully clarified before. In this study, we experimentally investigated the effects of display pixel arrays on the perception of glossiness and transparency. In a visual evaluation experiment, we investigated the effects of three types of sub-pixel arrays (RGB, RGBW, and PenTile) on the perception of glossiness and transparency using natural images. The results confirmed that sub-pixel arrays affect the appearance of glossiness and transparency. A general relationship of RGB > PenTile > RGBW for glossiness and RGB > RGBW > PenTile for transparency was found; however, detailed analysis, such as cluster analysis, confirmed that the relative superiority of these sub-pixel arrays may vary depending on the observer and image content. Full article
(This article belongs to the Special Issue Color in Image Processing and Computer Vision)
►▼ Show Figures

Figure 1

11 pages, 3199 KiB  
Communication
Accurate Determination of Camera Quantum Efficiency from a Single Image
by Yuri Rzhanov
J. Imaging 2024, 10(7), 169; https://doi.org/10.3390/jimaging10070169 - 16 Jul 2024
Viewed by 1370
Abstract
Knowledge of spectral sensitivity is important for high-precision comparison of images taken by different cameras and recognition of objects and interpretation of scenes for which color is an important cue. Direct estimation of quantum efficiency curves (QECs) is a complicated and tedious process [...] Read more.
Knowledge of spectral sensitivity is important for high-precision comparison of images taken by different cameras and recognition of objects and interpretation of scenes for which color is an important cue. Direct estimation of quantum efficiency curves (QECs) is a complicated and tedious process requiring specialized equipment, and many camera manufacturers do not make spectral characteristics publicly available. This has led to the development of indirect techniques that are unreliable due to being highly sensitive to noise in the input data, and which often require the imposition of additional ad hoc conditions, some of which do not always hold. We demonstrate the reason for the lack of stability in the determination of QECs and propose an approach that guarantees the stability of QEC reconstruction, even in the presence of noise. A device for the realization of this approach is also proposed. The reported results were used as a basis for the granted US patent. Full article
(This article belongs to the Special Issue Color in Image Processing and Computer Vision)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop