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Image Processing in Sensors and Communication Systems
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Image Processing in Sensors and Communication Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 7158

Special Issue Editor

Prof. Dr. Jiachen Yang

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin 300000, China
Interests: smart ocean system; intelligent monitoring; sensing network; Internet of Things; marine information processing; vision sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of communication technologies, more and more emerging technologies have been widely used, such as the Internet of Things, cloud desktop, etc., bringing great convenience to people’s lives. However, people’s attention to sensors and communication systems has changed from “usable” to “high-quality use”, and the existing sensors and communication systems often suffer from quality degradation caused by the source end and the transmission process, and lack reasonable quality assessment methods to evaluate the received content. It is worth noting that the addition of emerging intelligent technologies such as deep learning and big data technologies are conducive to promoting the quality of sensors and communication systems. Therefore, facing the urgent needs of life and technological development, it is necessary to carry out relevant research and propose emerging technologies in sensors and communication systems from the perspective of quality awareness and intelligent technologies, so as to improve the quality of relevant procedures and services.

This Special Issue focuses on image processing technologies in sensors and communication systems. We encourage researchers to propose algorithms, theories and standards that can help to improve the quality awareness of sensors and communication systems from the perspective of data and models. The ultimate goal is to provide a high-quality and stable environment for the next generation of sensors and communication systems. This Special Issue will highlight, but not be limited to, the following topics:

(1) Image compression algorithms in sensors and communication systems.

(2) Image quality assessment algorithms in sensors and communication systems.

(3) Image super-resolution methods in sensors and communication systems.

(4) Image encoding and decoding standards in sensors and communication systems.

(5) Proposals for a new generation of cloud desktop systems.

(6) Theories and standards in sensors and communication systems.

Prof. Dr. Jiachen Yang
Guest Editor

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. Sensors 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 2600 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 compression algorithms
  • image quality assessment algorithms
  • image super-resolution
  • cloud desktop systems
  • intelligent systems

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Published Papers (5 papers)

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Research

18 pages, 17281 KiB  
Article
ZYNQ-Based Visible Light Defogging System Design Realization
by Bohan Liu, Qihai Wei and Kun Ding
Sensors 2024, 24(7), 2276; https://doi.org/10.3390/s24072276 - 3 Apr 2024
Cited by 1 | Viewed by 946
Abstract
Under a foggy environment, the air contains a large number of suspended particles, which lead to the loss of image information and decline of contrast collected by the vision system. This makes subsequent processing and analysis difficult. At the same time, the current [...] Read more.
Under a foggy environment, the air contains a large number of suspended particles, which lead to the loss of image information and decline of contrast collected by the vision system. This makes subsequent processing and analysis difficult. At the same time, the current stage of the defogging system has problems such as high hardware cost and poor real-time processing. In this article, an image defogging system is designed based on the ZYNQ platform. First of all, on the basis of the traditional dark-channel defogging algorithm, an algorithm for segmenting the sky is proposed, and in this way, the image distortion caused by the sky region is avoided, and the atmospheric light value and transmittance are estimated more accurately. Then color balancing is performed after image defogging to improve the quality of the final output image. The parallel computing advantage and logic resources of the PL (Programmable Logic) part (FPGA) of ZYNQ are fully utilized through instruction constraints and logic optimization. Finally, the visible light detector is used as the input to build a real-time video processing experiment platform. The experimental results show that the system has a good defogging effect and meet the real-time requirements. Full article
(This article belongs to the Special Issue Image Processing in Sensors and Communication Systems)
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19 pages, 2592 KiB  
Article
Multispectral Demosaicing Based on Iterative-Linear-Regression Model for Estimating Pseudo-Panchromatic Image
by Kyeonghoon Jeong, Sanghoon Kim and Moon Gi Kang
Sensors 2024, 24(3), 760; https://doi.org/10.3390/s24030760 - 24 Jan 2024
Cited by 1 | Viewed by 1538
Abstract
This paper proposes a method for demosaicing raw images captured by multispectral cameras. The proposed method estimates a pseudo-panchromatic image (PPI) via an iterative-linear-regression model and utilizes the estimated PPI for multispectral demosaicing. The PPI is estimated through horizontal and vertical guided filtering, [...] Read more.
This paper proposes a method for demosaicing raw images captured by multispectral cameras. The proposed method estimates a pseudo-panchromatic image (PPI) via an iterative-linear-regression model and utilizes the estimated PPI for multispectral demosaicing. The PPI is estimated through horizontal and vertical guided filtering, with the subsampled multispectral-filter-array-(MSFA) image and low-pass-filtered MSFA as the guide image and filtering input, respectively. The number of iterations is automatically determined according to a predetermined criterion. Spectral differences between the estimated PPI and MSFA are calculated for each channel, and each spectral difference is interpolated using directional interpolation. The weights are calculated from the estimated PPI, and each interpolated spectral difference is combined using the weighted sum. The experimental results indicate that the proposed method outperforms the State-of-the-Art methods with regard to spatial and spectral fidelity for both synthetic and real-world images. Full article
(This article belongs to the Special Issue Image Processing in Sensors and Communication Systems)
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14 pages, 6241 KiB  
Article
Improving Recognition of Defective Epoxy Images in Integrated Circuit Manufacturing by Data Augmentation
by Lamia Alam and Nasser Kehtarnavaz
Sensors 2024, 24(3), 738; https://doi.org/10.3390/s24030738 - 23 Jan 2024
Cited by 2 | Viewed by 1013
Abstract
This paper discusses the problem of recognizing defective epoxy drop images for the purpose of performing vision-based die attachment inspection in integrated circuit (IC) manufacturing based on deep neural networks. Two supervised and two unsupervised recognition models are considered. The supervised models examined [...] Read more.
This paper discusses the problem of recognizing defective epoxy drop images for the purpose of performing vision-based die attachment inspection in integrated circuit (IC) manufacturing based on deep neural networks. Two supervised and two unsupervised recognition models are considered. The supervised models examined are an autoencoder (AE) network together with a multi-layer perceptron network (MLP) and a VGG16 network, while the unsupervised models examined are an autoencoder (AE) network together with k-means clustering and a VGG16 network together with k-means clustering. Since in practice very few defective epoxy drop images are available on an actual IC production line, the emphasis in this paper is placed on the impact of data augmentation on the recognition outcome. The data augmentation is achieved by generating synthesized defective epoxy drop images via our previously developed enhanced loss function CycleGAN generative network. The experimental results indicate that when using data augmentation, the supervised and unsupervised models of VGG16 generate perfect or near perfect accuracies for recognition of defective epoxy drop images for the dataset examined. More specifically, for the supervised models of AE+MLP and VGG16, the recognition accuracy is improved by 47% and 1%, respectively, and for the unsupervised models of AE+Kmeans and VGG+Kmeans, the recognition accuracy is improved by 37% and 15%, respectively, due to the data augmentation. Full article
(This article belongs to the Special Issue Image Processing in Sensors and Communication Systems)
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25 pages, 4382 KiB  
Article
Covert Communication through Robust Fragment Hiding in a Large Number of Images
by Pengfei Wang, Hua Zhong, Yapei Feng, Liangbiao Gong, Yuxiang Tang, Zhe-Ming Lu and Lixin Wang
Sensors 2024, 24(2), 627; https://doi.org/10.3390/s24020627 - 18 Jan 2024
Viewed by 1144
Abstract
For covert communication in lossy channels, it is necessary to consider that the carrier of the hidden watermark will undergo multiple image-processing attacks. In order to ensure that secret information can be extracted without distortion from the watermarked images that have undergone attacks, [...] Read more.
For covert communication in lossy channels, it is necessary to consider that the carrier of the hidden watermark will undergo multiple image-processing attacks. In order to ensure that secret information can be extracted without distortion from the watermarked images that have undergone attacks, in this paper, we design a novel fragmented secure communication system. The sender will fragment the secret data to be transmitted and redundantly hide it in a large number of multimodal carriers of messenger accounts on multiple social platforms. The receiver receives enough covert carriers, extracts each fragment, and concatenates the transmitted secret data. This article uses the image carrier as an example to fragment the text file intended for transmission and embeds it into a large number of images, with each fragment being redundant and embedded into multiple images. In this way, at the receiving end, only enough stego images need to be received to extract the information in each image, and then concatenate the final secret file. In order to resist various possible attacks during image transmission, we propose a strong robust image watermarking method. This method adopts a watermark layer based on DFT, which has high embedding and detection efficiency and good invisibility. Secondly, a watermark layer based on DCT is adopted, which can resist translation attacks, JPEG attacks, and other common attacks. Experiments have shown that our watermarking method is very fast; both the embedding time and the extraction time are less than 0.15 s for images not larger than 2000×2000. Our watermarking method has very good invisibility with 41 dB PSNR on average. And our watermarking method is more robust than existing schemes and robust to nearly all kinds of attacks. Based on this strong robust image watermarking method, the scheme of fragmenting and hiding redundant transmission content into a large number of images is effective and practical. Our scheme can 100% restore the secret file completely under different RST or hybrid attacks, such as rotation by 1 degree and 5 degrees, scaling by 1.25 and 0.8, and cropping by 10% and 25%. Our scheme can successfully restore the secret file completely even if 30% of received images are lost. When 80% of received images are lost, our scheme can still restore 61.1% of the secret file. If all stego images can be obtained, the original text file can be completely restored. Full article
(This article belongs to the Special Issue Image Processing in Sensors and Communication Systems)
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16 pages, 8787 KiB  
Communication
JPEG Image Enhancement with Pre-Processing of Color Reduction and Smoothing
by Akane Shoda, Tomo Miyazaki and Shinichiro Omachi
Sensors 2023, 23(21), 8861; https://doi.org/10.3390/s23218861 - 31 Oct 2023
Cited by 2 | Viewed by 1580
Abstract
JPEG is the international standard for still image encoding and is the most widely used compression algorithm because of its simple encoding process and low computational complexity. Recently, many methods have been developed to improve the quality of JPEG images by using deep [...] Read more.
JPEG is the international standard for still image encoding and is the most widely used compression algorithm because of its simple encoding process and low computational complexity. Recently, many methods have been developed to improve the quality of JPEG images by using deep learning. However, these methods require the use of high-performance devices since they need to perform neural network computation for decoding images. In this paper, we propose a method to generate high-quality images using deep learning without changing the decoding algorithm. The key idea is to reduce and smooth colors and gradient regions in the original images before JPEG compression. The reduction and smoothing can suppress red block noise and pseudo-contour in the compressed images. Furthermore, high-performance devices are unnecessary for decoding. The proposed method consists of two components: a color transformation network using deep learning and a pseudo-contour suppression model using signal processing. The experimental results showed that the proposed method outperforms standard JPEG in quality measurements correlated with human perception. Full article
(This article belongs to the Special Issue Image Processing in Sensors and Communication Systems)
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