Special Issue "Intelligent Media Processing"

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

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Prof. Dr. Shoko Imaizumi
E-Mail Website
Guest Editor
Department of Imaging Sciences, Graduate School of Science and Engineering, Chiba University, 1-33 Yayoicho, Inage-ku, Chiba-shi, Chiba, 263-8522, Japan
Interests: image processing; multimedia security; image coding
Prof. Dr. Keita Hirai
E-Mail Website
Guest Editor
Department of Imaging Sciences, Chiba University, Chiba, Japan
Interests: color image sensing, analysis, processing, reproduction, and evaluation

Special Issue Information

Dear Colleagues,

Thanks to the continuous progress and development of internet services and consumer equipment, multimedia images have become more common and, in fact, necessary in our daily life. Image processing is tackled in multiple fields, such as broadcast, printing, and storage, and applied to industry and social activities. Additionally, the integration of different types of media and cross-media strategies has triggered a new form of image distribution. To achieve further, advanced developments, mixed approaches in intelligent processing, cross-reality, soft computing, security, etc. are strongly required.

On another front, visual material appearance and affective engineering have also attracted a great deal of attention in human-centered imaging, and they are expected to contribute to high-value-added applications.

This Special Issue on “Intelligent Media Processing” is planned as a venue for presenting leading-edge research articles that may contribute to enhance the worth of digital images and multimedia. The topics of interest include but are not limited to:

  • Intelligent image processing;
  • Computer graphics;
  • Augmented reality/virtual reality/mixed reality;
  • Computer vision;
  • Deep learning;
  • High dynamic range images;
  • Security applications;
  • Image quality criteria;
  • Human-centered imaging;
  • Industrial applications.

Prof. Dr. Shoko Imaizumi
Prof. Dr. Keita Hirai
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 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 1600 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

  • Intelligent image processing
  • Computer graphics
  • Augmented reality/virtual reality/mixed reality
  • Computer vision
  • Deep learning
  • High dynamic range images
  • Security applications
  • Image quality criteria
  • Human-centered imaging
  • Industrial applications

Published Papers (1 paper)

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Research

Article
A Detection Method of Operated Fake-Images Using Robust Hashing
J. Imaging 2021, 7(8), 134; https://doi.org/10.3390/jimaging7080134 - 05 Aug 2021
Viewed by 440
Abstract
SNS providers are known to carry out the recompression and resizing of uploaded images, but most conventional methods for detecting fake images/tampered images are not robust enough against such operations. In this paper, we propose a novel method for detecting fake images, including [...] Read more.
SNS providers are known to carry out the recompression and resizing of uploaded images, but most conventional methods for detecting fake images/tampered images are not robust enough against such operations. In this paper, we propose a novel method for detecting fake images, including distortion caused by image operations such as image compression and resizing. We select a robust hashing method, which retrieves images similar to a query image, for fake-image/tampered-image detection, and hash values extracted from both reference and query images are used to robustly detect fake-images for the first time. If there is an original hash code from a reference image for comparison, the proposed method can more robustly detect fake images than conventional methods. One of the practical applications of this method is to monitor images, including synthetic ones sold by a company. In experiments, the proposed fake-image detection is demonstrated to outperform state-of-the-art methods under the use of various datasets including fake images generated with GANs. Full article
(This article belongs to the Special Issue Intelligent Media Processing)
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