Topic Editors

Dr. Jun Xu
School of Statistics and Data Science, Nankai University, Tianjin 300071, China
College of Software, Northeastern University, Shenyang 110819, China

Image Processing, Signal Processing and Their Applications

Abstract submission deadline
16 May 2026
Manuscript submission deadline
16 July 2026
Viewed by
419

Topic Information

Dear Colleagues,

Signal processing involves the analysis, modification, and synthesis of signals, such as sound, images, and scientific measurements. A variety of techniques are used to improve, extract, or compress information from raw data. Image processing, a subset of signal processing, focuses on visual data, such as photographs or video frames, and aims to enhance image quality, detect features, and transform images for interpretation or analysis. This Topic presents a wide range of research on image processing and signal processing, as well as their applications, covering the following subjects: Medical imaging; Speech and audio processing; Machine Learning for signal processing; Image and video processing; Image enhancement; Image restoration; Segmentation; Edge detection; Compression; Color image processing; Communications and networking; Computer vision; Multimedia.

Dr. Jun Xu
Prof. Dr. Lianbo Ma
Topic Editors

Keywords

  • medical imaging
  • speech and audio processing
  • machine learning for signal processing
  • image and video processing
  • image enhancement
  • image restoration
  • segmentation
  • edge detection
  • compression
  • color image processing
  • communications and networking
  • computer vision
  • multimedia

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.5 5.5 2011 19.8 Days CHF 2400 Submit
Information
information
2.9 6.5 2010 18.6 Days CHF 1800 Submit
Remote Sensing
remotesensing
4.1 8.6 2009 24.9 Days CHF 2700 Submit
Signals
signals
2.6 4.6 2020 22.9 Days CHF 1200 Submit
Symmetry
symmetry
2.2 5.3 2009 17.1 Days CHF 2400 Submit
Journal of Imaging
jimaging
3.3 6.7 2015 15.3 Days CHF 1800 Submit

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Published Papers (1 paper)

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19 pages, 12806 KiB  
Article
A Vision Method for Detecting Citrus Separation Lines Using Line-Structured Light
by Qingcang Yu, Song Xue and Yang Zheng
J. Imaging 2025, 11(8), 265; https://doi.org/10.3390/jimaging11080265 - 8 Aug 2025
Viewed by 208
Abstract
The detection of citrus separation lines is a crucial step in the citrus processing industry. Inspired by the achievements of line-structured light technology in surface defect detection, this paper proposes a method for detecting citrus separation lines based on line-structured light. Firstly, a [...] Read more.
The detection of citrus separation lines is a crucial step in the citrus processing industry. Inspired by the achievements of line-structured light technology in surface defect detection, this paper proposes a method for detecting citrus separation lines based on line-structured light. Firstly, a gamma-corrected Otsu method is employed to extract the laser stripe region from the image. Secondly, an improved skeleton extraction algorithm is employed to mitigate the bifurcation errors inherent in original skeleton extraction algorithms while simultaneously acquiring 3D point cloud data of the citrus surface. Finally, the least squares progressive iterative approximation algorithm is applied to approximate the ideal surface curve; subsequently, principal component analysis is used to derive the normals of this ideally fitted curve. The deviation between each point (along its corresponding normal direction) and the actual geometric characteristic curve is then adopted as a quantitative index for separation lines positioning. The average similarity between the extracted separation lines and the manually defined standard separation lines reaches 92.5%. In total, 95% of the points on the separation lines obtained by this method have an error of less than 4 pixels. Experimental results demonstrate that through quantitative deviation analysis of geometric features, automatic detection and positioning of the separation lines are achieved, satisfying the requirements of high precision and non-destructiveness for automatic citrus splitting. Full article
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