Image Processing, Signal Processing and Their Applications

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

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 (3 papers)

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All Applied Sciences Information Remote Sensing Signals Symmetry J. Imaging

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20 pages, 11332 KB  

Open AccessArticle

A Fast Nonlinear Sparse Model for Blind Image Deblurring

by Zirui Zhang, Zheng Guo, Zhenhua Xu, Huasong Chen, Chunyong Wang, Yang Song, Jiancheng Lai, Yunjing Ji and Zhenhua Li

J. Imaging 2025, 11(10), 327; https://doi.org/10.3390/jimaging11100327 - 23 Sep 2025

Viewed by 85

Abstract

Blind image deblurring, which requires simultaneous estimation of the latent image and blur kernel, constitutes a classic ill-posed problem. To address this, priors based on $L_2$, $L_1$, and $L_p$ regularizations have been widely adopted. Based on this foundation [...] Read more.

Blind image deblurring, which requires simultaneous estimation of the latent image and blur kernel, constitutes a classic ill-posed problem. To address this, priors based on $L_2$, $L_1$, and $L_p$ regularizations have been widely adopted. Based on this foundation and combining successful experiences of previous work, this paper introduces $L_N$ regularization, a novel nonlinear sparse regularization combining the $L_p$ and $L_{\mathrm{\infty }}$ norms via nonlinear coupling. Statistical probability analysis demonstrates that $L_N$ regularization achieves stronger sparsity than traditional regularizations like $L_2$, $L_1$, and $L_p$ regularizations. Furthermore, building upon the $L_N$ regularization, we propose a novel nonlinear sparse model for blind image deblurring. To optimize the proposed $L_N$ regularization, we introduce an Adaptive Generalized Soft-Thresholding (AGST) algorithm and further develop an efficient optimization strategy by integrating AGST with the Half-Quadratic Splitting (HQS) strategy. Extensive experiments conducted on synthetic datasets and real-world images demonstrate that the proposed nonlinear sparse model achieves superior deblurring performance while maintaining completive computational efficiency. Full article

(This article belongs to the Topic Image Processing, Signal Processing and Their Applications)

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17 pages, 8282 KB  

Open AccessArticle

Research on ADPLL for High-Precision Phase Measurement

by Weilai Yao, Chenying Sun, Xindong Liang and Jianjun Jia

Symmetry 2025, 17(9), 1487; https://doi.org/10.3390/sym17091487 - 8 Sep 2025

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Abstract

The inter-satellite laser interferometer, which functions as a high-performance displacement sensor, will be used in forthcoming space-based gravitational wave detection missions. The readout of these interferometers is typically performed by phasemeters based on all-digital phase-locked loops (ADPLLs) implemented in FPGAs. This paper proposes [...] Read more.

The inter-satellite laser interferometer, which functions as a high-performance displacement sensor, will be used in forthcoming space-based gravitational wave detection missions. The readout of these interferometers is typically performed by phasemeters based on all-digital phase-locked loops (ADPLLs) implemented in FPGAs. This paper proposes a feasible loop parameter design workflow and a comprehensive noise model, providing guidelines for designing and optimizing an ADPLL to meet specified bandwidth and precision requirements. The validity of our analysis is demonstrated through numerical performance measurements based on the modified digital splitting test. Full article

(This article belongs to the Topic Image Processing, Signal Processing and Their Applications)

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19 pages, 12806 KB  

Open AccessArticle

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 395

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

(This article belongs to the Topic Image Processing, Signal Processing and Their Applications)

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