Search for Topics:
Title/Keyword
Journal
Submission Status
Category
 
 
Topics
Color Image Processing: Models and Methods (CIP: MM)
Submit your Manuscript Submit your Abstract Edit a Topic

Topic Menu

► Topic Menu

Topic Editors

National Research Council, Institute for the Applications of Calculus "M. Picone", Via P. Castellino 111, 80131 Naples, Italy
National Research Council, Institute for the Applications of Calculus "M. Picone", Via P. Castellino 111, 80131 Naples, Italy
share announcement

Color Image Processing: Models and Methods (CIP: MM)

Abstract submission deadline
30 July 2023
Manuscript submission deadline
30 September 2023
Viewed by
2387

Topic Information

Dear Colleagues,

Color information plays a crucial role in digital image processing since it is a robust descriptor that can often improve data compression and simplify scene understanding for humans and automatic vision systems. Research about color presents new challenges since it makes it possible to expand the currently available methods, most of which are limited to the gray-level class of images. Furthermore, the multivariate nature of color image data requires the design of appropriate models and methods at both the mathematical and percentual/computational levels. As a result, Color Image Processing (CIP) has become an active research area witnessed by many papers during the past two decades. It finds wide application in numerous fields such as, among many others, Agriculture, Biomedicine, Cultural Heritage, Remote Sensing, Defense, and Security.

This Topic aims to give an overview of the state-of-the-art in color image processing and provide present/future directions in several applicative contexts. Specifically, the Topic focuses on two aspects that traditionally are considered separately: mathematical modeling and computational design of methods. Papers presenting reviews, alternative perspectives, new models/methods in the field of CIP facing both these aspects are welcome. All submitted papers will be peer-reviewed and selected on the basis of both their quality and relevance to the theme of this Topic.

We invite original contributions that provide novel solutions to these challenging problems. Submitted papers can address theoretical or practical aspects of progress and directions in CIP.

Issues of interest include, but are not limited to:

  • Information Theory and Entropy-based method for CIP
  • Color space models
  • Mathematical modeling for CIP
  • Numerical approximation for CIP
  • Color image enhancement, segmentation, and resizing
  • Data augmentation for CIP
  • Deep learning for CIP
  • Color content-based image retrieval
  • Color quality image assessment
  • Biometric CIP
  • Color medical imaging
  • CIP Models and Methods applied to Agriculture, Cultural Heritage, Remote Sensing, Defense, and Security

Dr. Giuliana Ramella
Dr. Isabella Torcicollo
Topic Editors

Keywords

  • color images
  • mathematical models
  • computational methods
  • color visual processing

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci 		2.838 3.7 2011 14.9 Days 2300 CHF Submit
Computation
computation 		- 3.3 2013 16.2 Days 1600 CHF Submit
Entropy
entropy 		2.738 4.4 1999 19.9 Days 2000 CHF Submit
Journal of Imaging
jimaging 		- 4.8 2015 21.2 Days 1600 CHF Submit

Preprints is a platform dedicated to making early versions of research outputs permanently available and citable. MDPI journals allow posting on preprint servers such as Preprints.org prior to publication. For more details about reprints, please visit https://www.preprints.org.

Published Papers (3 papers)

Order results
Result details
Journals
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
get_app
Article
Enhanced CycleGAN Network with Adaptive Dark Channel Prior for Unpaired Single-Image Dehazing
by
Yijun Xu
,
Hanzhi Zhang
,
Fuliang He
,
Jiachi Guo
and
Zichen Wang
Entropy 2023, 25(6), 856; https://doi.org/10.3390/e25060856 - 26 May 2023
Viewed by 206
Abstract
Unpaired single-image dehazing has become a challenging research hotspot due to its wide application in modern transportation, remote sensing, and intelligent surveillance, among other applications. Recently, CycleGAN-based approaches have been popularly adopted in single-image dehazing as the foundations of unpaired unsupervised training. However, [...] Read more.
Unpaired single-image dehazing has become a challenging research hotspot due to its wide application in modern transportation, remote sensing, and intelligent surveillance, among other applications. Recently, CycleGAN-based approaches have been popularly adopted in single-image dehazing as the foundations of unpaired unsupervised training. However, there are still deficiencies with these approaches, such as obvious artificial recovery traces and the distortion of image processing results. This paper proposes a novel enhanced CycleGAN network with an adaptive dark channel prior for unpaired single-image dehazing. First, a Wave-Vit semantic segmentation model is utilized to achieve the adaption of the dark channel prior (DCP) to accurately recover the transmittance and atmospheric light. Then, the scattering coefficient derived from both physical calculations and random sampling means is utilized to optimize the rehazing process. Bridged by the atmospheric scattering model, the dehazing/rehazing cycle branches are successfully combined to form an enhanced CycleGAN framework. Finally, experiments are conducted on reference/no-reference datasets. The proposed model achieved an SSIM of 94.9% and a PSNR of 26.95 on the SOTS-outdoor dataset and obtained an SSIM of 84.71% and a PSNR of 22.72 on the O-HAZE dataset. The proposed model significantly outperforms typical existing algorithms in both objective quantitative evaluation and subjective visual effect. Full article
(This article belongs to the Topic Color Image Processing: Models and Methods (CIP: MM))
►▼ Show Figures

Figure 1

get_app
Article
Manipulating Pixels in Computer Graphics by Converting Raster Elements to Vector Shapes as a Function of Hue
by
Tajana Koren Ivančević
,
Nikolina Stanić Loknar
,
Maja Rudolf
and
Diana Bratić
J. Imaging 2023, 9(6), 106; https://doi.org/10.3390/jimaging9060106 - 23 May 2023
Viewed by 278
Abstract
This paper proposes a method for changing pixel shape by converting a CMYK raster image (pixel) to an HSB vector image, replacing the square cells of the CMYK pixels with different vector shapes. The replacement of a pixel by the selected vector shape [...] Read more.
This paper proposes a method for changing pixel shape by converting a CMYK raster image (pixel) to an HSB vector image, replacing the square cells of the CMYK pixels with different vector shapes. The replacement of a pixel by the selected vector shape is done depending on the detected color values for each pixel. The CMYK values are first converted to the corresponding RGB values and then to the HSB system, and the vector shape is selected based on the obtained hue values. The vector shape is drawn in the defined space, according to the row and column matrix of the pixels of the original CMYK image. Twenty-one vector shapes are introduced to replace the pixels depending on the hue. The pixels of each hue are replaced by a different shape. The application of this conversion has its greatest value in the creation of security graphics for printed documents and the individualization of digital artwork by creating structured patterns based on the hue. Full article
(This article belongs to the Topic Color Image Processing: Models and Methods (CIP: MM))
►▼ Show Figures

Graphical abstract

get_app
Article
Comparing Different Algorithms for the Pseudo-Coloring of Myocardial Perfusion Single-Photon Emission Computed Tomography Images
by
Abdurrahim Rahimian
,
Mahnaz Etehadtavakol
,
Masoud Moslehi
and
Eddie Y. K. Ng
J. Imaging 2022, 8(12), 331; https://doi.org/10.3390/jimaging8120331 - 19 Dec 2022
Viewed by 963
Abstract
Single-photon emission computed tomography (SPECT) images can significantly help physicians in diagnosing patients with coronary artery or suspected coronary artery diseases. However, these images are grayscale with qualities that are not readily visible. The objective of this study was to evaluate the effectiveness [...] Read more.
Single-photon emission computed tomography (SPECT) images can significantly help physicians in diagnosing patients with coronary artery or suspected coronary artery diseases. However, these images are grayscale with qualities that are not readily visible. The objective of this study was to evaluate the effectiveness of different pseudo-coloring algorithms of myocardial perfusion SPECT images. Data were collected using a Siemens Symbia T2 dual-head SPECT/computed tomography (CT) scanner. After pseudo-coloring, the images were assessed both qualitatively and quantitatively. The qualities of different pseudo-color images were examined by three experts, while the images were evaluated quantitatively by obtaining indices such as mean squared error (MSE), peak signal-to-noise ratio (PSNR), normalized color difference (NCD), and structure similarity index metric (SSIM). The qualitative evaluation demonstrated that the warm color map (WCM), followed by the jet color map, outperformed the remaining algorithms in terms of revealing the non-visible qualities of the images. Furthermore, the quantitative evaluation results demonstrated that the WCM had the highest PSNR and SSIM but the lowest MSE. Overall, the WCM could outperform the other color maps both qualitatively and quantitatively. The novelty of this study includes comparing different pseudo-coloring methods to improve the quality of myocardial perfusion SPECT images and utilizing our collected datasets. Full article
(This article belongs to the Topic Color Image Processing: Models and Methods (CIP: MM))
►▼ 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