New Insights into Digital Image Processing and Denoising

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 October 2024 | Viewed by 598

Special Issue Editors


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Guest Editor
Department of Electronics and Intelligent Systems, Kielce University of Technology, Kielce, Poland
Interests: visual content analysis; image features detection; image classification and recognition; image segmentation; image compression; contour extraction and approximation

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Guest Editor
Institute of Telecommunications, AGH University of Science and Technology, Krakow, Poland
Interests: digital communication; image and data processing; intelligent monitoring; security systems; information and coding theory; random signals; computer communications networks and signal processing; watermarking technology

Special Issue Information

Dear Colleagues,

Nowadays, in the era of cutting-edge technologies, the implementation of digital image processing techniques has become a key element of many solutions. This is because there are several potential innovative digital imaging applications that include different areas such as geographic information systems (GIS), environmental and traffic monitoring, quality control in production automation, medical diagnosis and treatment, autonomous vehicles, defense vision, robotics, remote sensing, art and cultural heritage preservation and exploitation, detecting crime and threats, 3D modeling and multimedia, etc.

The growing trends and requirements of the above-mentioned image applications are an increasing challenge for researchers and engineers dealing with digital image processing (DIP). Therefore, DIP is a multidisciplinary science that employs knowledge from various fields such as computer science, mathematics, and also visual psychophysics.

The aim of this Special Issue is to bring together high-quality scientific papers and reports referring to the latest and the most innovative research from a wide range of specialists that could provide comprehensive insights into modern approaches of digital image processing and denoising.

The topics of this Special Issue include (but are not limited to) the following:

  • Advanced image (and video) enhancement, denoising, and restoration;
  • Image segmentation and object extraction;
  • Image classification and recognition;
  • Image transforms and manipulations;
  • Machine learning for image processing;
  • Neural computing for image processing;
  • Evolutionary algorithms for image processing;
  • Object recognition and image tagging;
  • Remote sensing image classification;
  • Medical image classification;
  • Face and person recognition;
  • Handwriting recognition (HWR);
  • Content-based image retrieval;
  • Building (and other GIS objects) footprint extraction from LIDAR and photogrammetric data;
  • 3D imaging, modeling, and visualization;
  • Image augmentation and virtual reality;
  • Watermarking techniques for data hiding and privacy protection;
  • Video surveillance, object tracking, and threat detection;
  • Video indexing and abstraction;
  • Image quality assessment (IQA);
  • Prediction of plant‒leaf disease.

Dr. Remigiusz Baran
Prof. Dr. Andrzej Dziech
Guest Editors

Manuscript Submission Information

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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

  • image processing
  • image enhancement
  • image recognition
  • image augmentation
  • image retrieval
  • object instance segmentation
  • shallow and deep learning
  • CNN
  • filter banks
  • 3D imaging

Published Papers (1 paper)

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Research

13 pages, 2178 KiB  
Article
A Novel Method Combining U-Net with LSTM for Three-Dimensional Soil Pore Segmentation Based on Computed Tomography Images
by Lei Liu, Qiaoling Han, Yue Zhao and Yandong Zhao
Appl. Sci. 2024, 14(8), 3352; https://doi.org/10.3390/app14083352 - 16 Apr 2024
Viewed by 257
Abstract
The non-destructive study of soil micromorphology via computed tomography (CT) imaging has yielded significant insights into the three-dimensional configuration of soil pores. Precise pore analysis is contingent on the accurate transformation of CT images into binary image representations. Notably, segmentation of 2D CT [...] Read more.
The non-destructive study of soil micromorphology via computed tomography (CT) imaging has yielded significant insights into the three-dimensional configuration of soil pores. Precise pore analysis is contingent on the accurate transformation of CT images into binary image representations. Notably, segmentation of 2D CT images frequently harbors inaccuracies. This paper introduces a novel three-dimensional pore segmentation method, BDULSTM, which integrates U-Net with convolutional long short-term memory (CLSTM) networks to harness sequence data from CT images and enhance the precision of pore segmentation. The BDULSTM method employs an encoder–decoder framework to holistically extract image features, utilizing skip connections to further refine the segmentation accuracy of soil structure. Specifically, the CLSTM component, critical for analyzing sequential information in soil CT images, is strategically positioned at the juncture of the encoder and decoder within the U-shaped network architecture. The validation of our method confirms its efficacy in advancing the accuracy of soil pore segmentation beyond that of previous deep learning techniques, such as U-Net and CLSTM independently. Indeed, BDULSTM exhibits superior segmentation capabilities across a diverse array of soil conditions. In summary, BDULSTM represents a state-of-the-art artificial intelligence technology for the 3D segmentation of soil pores and offers a promising tool for analyzing pore structure and soil quality. Full article
(This article belongs to the Special Issue New Insights into Digital Image Processing and Denoising)
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