Special Issue "Soft Computing in Image Processing"

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

Deadline for manuscript submissions: closed (31 May 2016)

Special Issue Editor

Guest Editor
Prof. Dr. Erik Cuevas

Department of Electronics, Universidad de Guadalajara, Av.Revolucion 1500, Guadalajara, Mexico
Website | E-Mail
Interests: computer vision; evolutionary computation; artificial intelligence; bio-inspired computation

Special Issue Information

Dear Colleagues,

Images affect every aspect of modern society. The cheap collection, storage, and transmission of vast amounts of them have revolutionized the practice of science, technology, and business. Innovations from various disciplines have been developed and applied to the task of designing systems that can automatically detect and exploit useful information in images.

In recent years there has been a growing interest in the need of using soft computing approaches to solve image processing problems. One of the most challenging issues with this integration is to effectively handle image uncertainties that cannot be eliminated. These uncertainties include various types of information that are incomplete, noisy, imprecise, fragmentary, not fully reliable, vague, contradictory, deficient, and overloading. They result in a lack of the full and precise knowledge of the system, including the determining and selection of evaluation criteria, alternatives, weights, assignment scores, and the final integrated decision result. Soft computing techniques, including fuzzy logic, neural networks, evolutionary methods, etc., as alternatives to the existing classical techniques, have shown great potential to solve image processing problems under such conditions.

This Special Issue aims to provide a collection of high quality research articles that address broad challenges in both theoretical and application aspects of soft computing in image processing. We invite colleagues to contribute original research articles, as well as review articles, that will stimulate the continuing effort on the application of soft computing approaches to solve image-processing problems.

Potential topics include, but are not limited to:

The use of computational intelligence techniques such as:

  • Neural networks
  • Fuzzy logic
  • Rough sets
  • Evolutionary methods
  • Expert system


  • Coding and compression
  • Sampling and interpolation
  • Quantization and halftoning
  • Quality assessment
  • Filtering and enhancement
  • Morphology
  • Edge detection and segmentation
  • Feature extraction
  • Indexing and retrieval

Prof. Dr. Erik Cuevas
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 papers will be 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 350 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.


  • Soft Computing
  • Image processing
  • Intelligent image processing
  • Artificial Intelligence

Published Papers (1 paper)

View options order results:
result details:
Displaying articles 1-1
Export citation of selected articles as:


Open AccessCommunication
Quality Control of Slot-Die Coated Aluminum Oxide Layers for Battery Applications Using Hyperspectral Imaging
J. Imaging 2016, 2(2), 12; https://doi.org/10.3390/jimaging2020012
Received: 30 November 2015 / Revised: 23 March 2016 / Accepted: 1 April 2016 / Published: 7 April 2016
Cited by 2 | PDF Full-text (5083 KB) | HTML Full-text | XML Full-text | Supplementary Files
Hyperspectral inspection using imaging systems is becoming more and more important for quality control tasks in several industries, replacing well trained operators or established machine vision systems based on RGB-systems. Hyperspectral imaging (HSI) on thin coated substrates is challenging due to the high [...] Read more.
Hyperspectral inspection using imaging systems is becoming more and more important for quality control tasks in several industries, replacing well trained operators or established machine vision systems based on RGB-systems. Hyperspectral imaging (HSI) on thin coated substrates is challenging due to the high reflectivity of the substrates. Nevertheless, the thin films contribute to the spectral data and can be evaluated. Therefore, the performance of inspection systems can be increased significantly. However, the large amount of data generated by HSI has to be processed and evaluated for quality information about the product. In this paper, thin aluminum oxide (Al2O3) layers on stainless steel foil are investigated by HSI. These substrates can be used for the growth of vertically aligned carbon nanotubes (VA-SWCNT) for battery electrodes. HSI and spectral ellipsometry in combination with Partial Least Squares regression (PLS) was used to estimate the thickness of the Al2O3 layers and to calculate quality parameters for a possible monitoring process. The PLS model shows a R2CV of 0.979 and a RMSECV of 3.6. Full article
(This article belongs to the Special Issue Soft Computing in Image Processing)

Figure 1

J. Imaging EISSN 2313-433X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top