Imaging and Color Vision

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 (30 June 2023) | Viewed by 5485

Special Issue Editors


E-Mail Website
Guest Editor
TexColImag group, Departament of Optics, University of Granada, 18071 Granada, Spain
Interests: color difference formulas; color spaces; color vision deficiencies; color in images; texture; food color

E-Mail Website
Guest Editor
Departamento de Matemática Aplicada, Universidad Politécnica de Valencia, Camino de Vera, s/n, 46022 Valencia, Spain
Interests: fuzzy logic; image processing; vision science; perceptual imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

On the one hand, traditionally colorimetry, particularly research in color differences and color spaces, has been working with just plain samples. On the other hand, research in imaging has used rudimentary colorimetry as just the RGB values of the images, far from the perception of color by the human being. Clearly both research field are closely related and in the last years have been progressing and converging. The aim of this Special Issue, Imaging and Color Vision, is to joint both fields enriching from each other.

Researchers are invited to submit original research and review articles regarding techniques and results in several areas, including the quality of images (regarding color), visual color differences in images, color vision deficiency models, image recoloring for color vision deficiencies, medical images, hyperspectral images, image comprension, etc.

Dr. Rafael Huertas
Dr. Samuel Morillas Gómez
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

  • CIELAB color coordinates
  • Color Appearance Models (CIECAM02)
  • cromatic adaptation
  • image recoloring for color vision deficiency compensation
  • medical imaging
  • image comprension
  • hyperspectral and multispectral images
  • image processing

Published Papers (3 papers)

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

Research

15 pages, 4126 KiB  
Article
Performance Comparison of Classical Methods and Neural Networks for Colour Correction
by Abdullah Kucuk, Graham D. Finlayson, Rafal Mantiuk and Maliha Ashraf
J. Imaging 2023, 9(10), 214; https://doi.org/10.3390/jimaging9100214 - 07 Oct 2023
Viewed by 1647
Abstract
Colour correction is the process of converting RAW RGB pixel values of digital cameras to a standard colour space such as CIE XYZ. A range of regression methods including linear, polynomial and root-polynomial least-squares have been deployed. However, in recent years, various neural [...] Read more.
Colour correction is the process of converting RAW RGB pixel values of digital cameras to a standard colour space such as CIE XYZ. A range of regression methods including linear, polynomial and root-polynomial least-squares have been deployed. However, in recent years, various neural network (NN) models have also started to appear in the literature as an alternative to classical methods. In the first part of this paper, a leading neural network approach is compared and contrasted with regression methods. We find that, although the neural network model supports improved colour correction compared with simple least-squares regression, it performs less well than the more advanced root-polynomial regression. Moreover, the relative improvement afforded by NNs, compared to linear least-squares, is diminished when the regression methods are adapted to minimise a perceptual colour error. Problematically, unlike linear and root-polynomial regressions, the NN approach is tied to a fixed exposure (and when exposure changes, the afforded colour correction can be quite poor). We explore two solutions that make NNs more exposure-invariant. First, we use data augmentation to train the NN for a range of typical exposures and second, we propose a new NN architecture which, by construction, is exposure-invariant. Finally, we look into how the performance of these algorithms is influenced when models are trained and tested on different datasets. As expected, the performance of all methods drops when tested with completely different datasets. However, we noticed that the regression methods still outperform the NNs in terms of colour correction, even though the relative performance of the regression methods does change based on the train and test datasets. Full article
(This article belongs to the Special Issue Imaging and Color Vision)
Show Figures

Figure 1

24 pages, 5980 KiB  
Article
A Model of Pixel and Superpixel Clustering for Object Detection
by Vadim A. Nenashev, Igor G. Khanykov and Mikhail V. Kharinov
J. Imaging 2022, 8(10), 274; https://doi.org/10.3390/jimaging8100274 - 06 Oct 2022
Cited by 4 | Viewed by 1435
Abstract
The paper presents a model of structured objects in a grayscale or color image, described by means of optimal piecewise constant image approximations, which are characterized by the minimum possible approximation errors for a given number of pixel clusters, where the approximation error [...] Read more.
The paper presents a model of structured objects in a grayscale or color image, described by means of optimal piecewise constant image approximations, which are characterized by the minimum possible approximation errors for a given number of pixel clusters, where the approximation error means the total squared error. An ambiguous image is described as a non-hierarchical structure but is represented as an ordered superposition of object hierarchies, each containing at least one optimal approximation in g0 = 1, 2,..., etc., colors. For the selected hierarchy of pixel clusters, the objects-of-interest are detected as the pixel clusters of optimal approximations, or as their parts, or unions. The paper develops the known idea in cluster analysis of the joint application of Ward’s and K-means methods. At the same time, it is proposed to modernize each of these methods and supplement them with a third method of splitting/merging pixel clusters. This is useful for cluster analysis of big data described by a convex dependence of the optimal approximation error on the cluster number and also for adjustable object detection in digital image processing, using the optimal hierarchical pixel clustering, which is treated as an alternative to the modern informally defined “semantic” segmentation. Full article
(This article belongs to the Special Issue Imaging and Color Vision)
Show Figures

Figure 1

22 pages, 14201 KiB  
Article
Local Contrast-Based Pixel Ordering for Exact Histogram Specification
by Kohei Inoue, Naoki Ono and Kenji Hara
J. Imaging 2022, 8(9), 247; https://doi.org/10.3390/jimaging8090247 - 10 Sep 2022
Viewed by 1695
Abstract
Histogram equalization is one of the basic image processing tasks for contrast enhancement, and its generalized version is histogram specification, which accepts arbitrary shapes of target histograms including uniform distributions for histogram equalization. It is well known that strictly ordered pixels in an [...] Read more.
Histogram equalization is one of the basic image processing tasks for contrast enhancement, and its generalized version is histogram specification, which accepts arbitrary shapes of target histograms including uniform distributions for histogram equalization. It is well known that strictly ordered pixels in an image can be voted to any target histogram to achieve exact histogram specification. This paper proposes a method for ordering pixels in an image on the basis of the local contrast of each pixel, where a Gaussian filter without approximation is used to avoid the duplication of pixel values that disturbs the strict pixel ordering. The main idea of the proposed method is that the problem of pixel ordering is divided into small subproblems which can be solved separately, and then the results are merged into one sequence of all ordered pixels. Moreover, the proposed method is extended from grayscale images to color ones in a consistent manner. Experimental results show that the state-of-the-art histogram specification method occasionally produces false patterns, which are alleviated by the proposed method. Those results demonstrate the effectiveness of the proposed method for exact histogram specification. Full article
(This article belongs to the Special Issue Imaging and Color Vision)
Show Figures

Figure 1

Back to TopTop