Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Digital Image Processing: Technologies and Applications
share announcement

Digital Image Processing: Technologies and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 3488

Special Issue Editors

Prof. Dr. Héctor Manuel Pérez-Meana

E-Mail Website
Guest Editor
Instituto Politécnico Nacional, Av. Santa Ana 1000, Coyoacan, Mexico City CP4040, Mexico
Interests: compressive sensing; speech recognition; digital watermarking; data hiding; speech processing; digital image processing
Prof. Dr. Mariko Nakano-Miyatake

E-Mail Website
Guest Editor
Instituto Politécnico Nacional, Av. Santa Ana 1000, Coyoacan, Mexico City CP4040, Mexico
Interests: medical images; pattern recognition; digital watermarking; data hiding; deep learning; digital image processing

Special Issue Information

Dear Colleagues,

Digital image processing has been a topic of active research for many years, and has found application in several fields.  In recent years, with the advance of computer technology the use of image processing technology has widely spread in fields, such as information security, biometrics, medicine, image compression, restoration, access control, pattern recognition, image synthesis, and image understanding, among others.

We would like to invite the academic and industrial research community to submit original research, as well as review articles to this Special Issue. Topics include:

  • Biometric pattern recognition;
  • Compressive sensing applications;
  • Digital watermarking;
  • Image classification;
  • Image clustering;
  • Image restoration;
  • Image authentication;
  • Image denoising;
  • Image compression;
  • Image encryption;
  • Face expression recognition;
  • Deep learning-based pattern recognition;
  • 3D image processing;
  • Medical image analysis.

Prof. Dr. Héctor Manuel Pérez-Meana
Prof. Dr. Mariko Nakano-Miyatake
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • biometrics
  • pattern recognition
  • enhancement
  • compression
  • authentication
  • encryption
  • watermarking
  • restoration

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

21 pages, 6212 KiB  
Article
High-Noise Grayscale Image Denoising Using an Improved Median Filter for the Adaptive Selection of a Threshold
by Ning Cao and Yupu Liu
Appl. Sci. 2024, 14(2), 635; https://doi.org/10.3390/app14020635 - 11 Jan 2024
Cited by 1 | Viewed by 825
Abstract
Grayscale image processing is a key research area in the field of computer vision and image analysis, where image quality and visualization effects may be seriously damaged by high-density salt and pepper noise. A traditional median filter for noise removal may result in [...] Read more.
Grayscale image processing is a key research area in the field of computer vision and image analysis, where image quality and visualization effects may be seriously damaged by high-density salt and pepper noise. A traditional median filter for noise removal may result in poor detail reservation performance under strong noise and the judgment performance of different noise characteristics has strong dependence and rather weak robustness. In order to reduce the effects of high-density salt and pepper noise on image quality when processing high-noise grayscale images, an improved two-dimensional maximum Shannon entropy median filter (TSETMF) is proposed for the adaptive selection of a threshold to enhance the filter performance while stably and effectively retaining the details of the images. The framework of the proposed improved TSETMF algorithm is designed in detail. The noise in images is filtered by means of automatically partitioning a window size, the threshold value of which is adaptively calculated using two-dimensional maximum Shannon entropy. The theoretical model is verified and analyzed through comparative experiments using three kinds of classical grayscale images. The experimental results demonstrate that the proposed improved TSETMF algorithm exhibits better processing performance than that of the traditional filter, with a higher suppression of high-density noise and denoising stability. This stronger ability while processing high-density noise is demonstrated by a higher peak signal-to-noise ratio (PSNR) of 24.97 dB with a 95% noise density located in the classical Lena grayscale image. The better denoising stability, with a noise density from 5% to 95%, is demonstrated by the minor decline in the PSNR of approximately 10.78% relative to a PSNR of 23.10 dB located in the classical Cameraman grayscale image. Furthermore, it can be advanced to promote higher noise filtering and stability for processing high-density salt and pepper noise in grayscale images. Full article
(This article belongs to the Special Issue Digital Image Processing: Technologies and Applications)
►▼ Show Figures

Figure 1

22 pages, 11592 KiB  
Article
Genetic Programming to Remove Impulse Noise in Color Images
by Daniel Fajardo-Delgado, Ansel Y. Rodríguez-González, Sergio Sandoval-Pérez, Jesús Ezequiel Molinar-Solís and María Guadalupe Sánchez-Cervantes
Appl. Sci. 2024, 14(1), 126; https://doi.org/10.3390/app14010126 - 22 Dec 2023
Viewed by 675
Abstract
This paper presents a new filter to remove impulse noise in digital color images. The filter is adaptive in the sense that it uses a detection stage to only correct noisy pixels. Detecting noisy pixels is performed by a binary classification model generated [...] Read more.
This paper presents a new filter to remove impulse noise in digital color images. The filter is adaptive in the sense that it uses a detection stage to only correct noisy pixels. Detecting noisy pixels is performed by a binary classification model generated via genetic programming, a paradigm of evolutionary computing based on natural biological selection. The classification model training considers three impulse noise models in color images: salt and pepper, uniform, and correlated. This is the first filter generated by genetic programming exploiting the correlation among the color image channels. The correction stage consists of a vector median filter version that modifies color channel values if some are noisy. An experimental study was performed to compare the proposed filter with others in the state-of-the-art related to color image denoising. Their performance was measured objectively through the image quality metrics PSNR, MAE, SSIM, and FSIM. Experimental findings reveal substantial variability among filters based on noise model and image characteristics. The findings also indicate that, on average, the proposed filter consistently exhibited top-tier performance values for the three impulse noise models, surpassed only by a filter employing a deep learning-based approach. Unlike deep learning filters, which are black boxes with internal workings invisible to the user, the proposed filter has a high interpretability with a performance close to an equilibrium point for all images and noise models used in the experiment. Full article
(This article belongs to the Special Issue Digital Image Processing: Technologies and Applications)
►▼ Show Figures

Figure 1

20 pages, 7517 KiB  
Article
Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering
by Guiwei Fu, Yujin Zhang and Yongqi Wang
Appl. Sci. 2023, 13(13), 7528; https://doi.org/10.3390/app13137528 - 26 Jun 2023
Cited by 2 | Viewed by 1424
Abstract
Image copy-move forgery is a common simple tampering technique. To address issues such as high time complexity in most copy-move forgery detection algorithms and difficulty detecting forgeries in smooth regions, this paper proposes an image copy-move forgery detection algorithm based on fused features [...] Read more.
Image copy-move forgery is a common simple tampering technique. To address issues such as high time complexity in most copy-move forgery detection algorithms and difficulty detecting forgeries in smooth regions, this paper proposes an image copy-move forgery detection algorithm based on fused features and density clustering. Firstly, the algorithm combines two detection methods, speeded up robust features (SURF) and accelerated KAZE (A-KAZE), to extract descriptive features by setting a low contrast threshold. Then, the density-based spatial clustering of applications with noise (DBSCAN) algorithm removes mismatched pairs and reduces false positives. To improve the accuracy of forgery localization, the algorithm uses the original image and the image transformed by the affine matrix to compare similarities in the same position in order to locate the forged region. The proposed method was tested on two datasets (Ardizzone and CoMoFoD). The experimental results show that the method effectively improved the accuracy of forgery detection in smooth regions, reduced computational complexity, and exhibited strong robustness against post-processing operations such as rotation, scaling, and noise addition. Full article
(This article belongs to the Special Issue Digital Image Processing: Technologies and Applications)
►▼ 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