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Computational Sensing and Imaging
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Computational Sensing and Imaging

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

Deadline for manuscript submissions: closed (20 July 2022) | Viewed by 16417

Special Issue Editor

Prof. Dr. Hoon Yoo

E-Mail Website
Guest Editor
Department of Electronics Engineering, Sangmyung University, 20 Hongjimoon-2gil, Seoul 030031, Republic of Korea
Interests: image processing; 3D imaging; computational reconstruction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational sensing and imaging play a major role in imaging techniques and have many applications in areas of medical imaging, 3D imaging, lensless imaging, synthetic aperture radar imaging, seismic imaging, ultrasound imaging and so on. In addition, a lot of research has accelerated the sensing and imaging performance of those applications in the last few years. Specifically, this Special Issue is focused on image processing to obtain high-quality images and to remedy various problems to deteriorate sensing and imaging performance of physical or optical devices in terms of image quality, imaging speed, and functionality.

The purpose of this Special Issue is to broadly engage the communities of image processing and signal sensing to provide a forum for the researchers and engineers related to this rapidly developing field and share their novel and original research regarding the topic of computational sensing and imaging. Survey papers addressing relevant topics are also welcome. 

Prof. Dr. Hoon Yoo
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.

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Keywords

  • computational photography for 3D imaging
  • depth estimation and three-dimensional sensing
  • medical imaging (CT/MRI/PET image reconstruction)
  • image restoration and denoising
  • image registration and super-resolution imaging
  • high-speed imaging systems and bandwidth reduction
  • computational sensing for advanced driver assistance systems (ADAS)
  • synthetic aperture radar (SAR) imaging
  • seismic imaging
  • ultrasound imaging
  • computational sensing for advanced image signal processor (ISP)
  • deep learning for image reconstruction
  • remote sensing and UAV image processing
  • under-water imaging and dehazing

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Published Papers (5 papers)

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Research

11 pages, 2121 KiB  
Article
Re-Calibration and Lens Array Area Detection for Accurate Extraction of Elemental Image Array in Three-Dimensional Integral Imaging
by Hyeonah Jeong, Eunsu Lee and Hoon Yoo
Appl. Sci. 2022, 12(18), 9252; https://doi.org/10.3390/app12189252 - 15 Sep 2022
Cited by 1 | Viewed by 1065
Abstract
This paper presents a new method for extracting an elemental image array in three-dimensional (3D) integral imaging. To reconstruct 3D images in integral imaging, as the first step, a method is required to accurately extract an elemental image array from a raw captured [...] Read more.
This paper presents a new method for extracting an elemental image array in three-dimensional (3D) integral imaging. To reconstruct 3D images in integral imaging, as the first step, a method is required to accurately extract an elemental image array from a raw captured image. Thus, several methods have been discussed to extract an elemental image array. However, the accuracy is sometimes degraded due to inaccurate edge detection, image distortions, optical misalignment, and so on. Especially, small pixel errors can deteriorate the performance of an integral imaging system with a lens array. To overcome the problem, we propose a postprocessing method for the accurate extraction of an elemental image array. Our method is a unified version of an existing method and proposed postprocessing techniques. The proposed postprocessing consists of re-calibration and lens array area detection. Our method reuses the results from an existing method, and it then improves the results via the proposed postprocessing techniques. To evaluate the proposed method, we perform optical experiments for 3D objects and provide the resulting images. The experimental results indicate that the proposed postprocessing techniques improve an existing method for extracting an elemental image array in integral imaging. Therefore, we expect the proposed techniques to be applied to various applications of integral imaging systems Full article
(This article belongs to the Special Issue Computational Sensing and Imaging)
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22 pages, 7949 KiB  
Article
Target-Oriented High-Resolution and Wide-Swath Imaging with an Adaptive Receiving–Processing–Decision Feedback Framework
by Xu Zhan, Xiaoling Zhang, Wensi Zhang, Yuetonghui Xu, Jun Shi, Shunjun Wei and Tianjiao Zeng
Appl. Sci. 2022, 12(17), 8922; https://doi.org/10.3390/app12178922 - 5 Sep 2022
Cited by 2 | Viewed by 1471
Abstract
High-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) is a promising technique for applications such as maritime surveillance. In the maritime environment, normally only a few targets such as ships are interested. However, before detecting them, considerable receiving resources and computation time are [...] Read more.
High-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) is a promising technique for applications such as maritime surveillance. In the maritime environment, normally only a few targets such as ships are interested. However, before detecting them, considerable receiving resources and computation time are required to receive the echoes of the whole scene and process them to obtain imaging results. This is a heavy burden for online monitoring on platforms with limited resources. To address these issues, different from the concept of whole-scene-oriented imaging, we propose a target-oriented imaging concept, which is implemented by an adaptive receiving–processing–decision feedback framework with feedback connection. (1) To reduce receiving resource consumption, we propose a two-dimensional adaptive receiving module. It receives sub-band echoes of targets only through dechirping and subaperture decomposition in the range and azimuth directions, respectively. (2) To reduce computation time, we propose a target-oriented processing module. It processes subarea echoes of targets only through parallelly conducting inverse fast Fourier transform (IFFT) and back projection (BP) in the range and azimuth directions, respectively. (3) To allocate resources reasonably, we propose a decision module. It decides the necessary receiving window, bandwidth, and image resolution through constant false alarm rate (CFAR) detection. (4) To allocate resources adaptively, we connect three modules with a closed loop to enable feedback. This enables progressive target imaging and detection from rough to fine. Experimental results verify the feasibility of the proposed framework. Compared with the current one, for a typical scenario, at least 30% of the system’s resources and 99% of computation time are saved. Full article
(This article belongs to the Special Issue Computational Sensing and Imaging)
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19 pages, 6908 KiB  
Article
Reverse Image Search Using Deep Unsupervised Generative Learning and Deep Convolutional Neural Network
by Aqsa Kiran, Shahzad Ahmad Qureshi, Asifullah Khan, Sajid Mahmood, Muhammad Idrees, Aqsa Saeed, Muhammad Assam, Mohamad Reda A. Refaai and Abdullah Mohamed
Appl. Sci. 2022, 12(10), 4943; https://doi.org/10.3390/app12104943 - 13 May 2022
Cited by 3 | Viewed by 3452
Abstract
Reverse image search has been a vital and emerging research area of information retrieval. One of the primary research foci of information retrieval is to increase the space and computational efficiency by converting a large image database into an efficiently computed feature database. [...] Read more.
Reverse image search has been a vital and emerging research area of information retrieval. One of the primary research foci of information retrieval is to increase the space and computational efficiency by converting a large image database into an efficiently computed feature database. This paper proposes a novel deep learning-based methodology, which captures channel-wise, low-level details of each image. In the first phase, sparse auto-encoder (SAE), a deep generative model, is applied to RGB channels of each image for unsupervised representational learning. In the second phase, transfer learning is utilized by using VGG-16, a variant of deep convolutional neural network (CNN). The output of SAE combined with the original RGB channel is forwarded to VGG-16, thereby producing a more effective feature database by the ensemble/collaboration of two effective models. The proposed method provides an information rich feature space that is a reduced dimensionality representation of the image database. Experiments are performed on a hybrid dataset that is developed by combining three standard publicly available datasets. The proposed approach has a retrieval accuracy (precision) of 98.46%, without using the metadata of images, by using a cosine similarity measure between the query image and the image database. Additionally, to further validate the proposed methodology’s effectiveness, image quality has been degraded by adding 5% noise (Speckle, Gaussian, and Salt pepper noise types) in the hybrid dataset. Retrieval accuracy has generally been found to be 97% for different variants of noise Full article
(This article belongs to the Special Issue Computational Sensing and Imaging)
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21 pages, 4249 KiB  
Article
Risk and Pattern Analysis of Pakistani Crime Data Using Unsupervised Learning Techniques
by Faria Ferooz, Malik Tahir Hassan, Sajid Mahmood, Hira Asim, Muhammad Idrees, Muhammad Assam, Abdullah Mohamed and El-Awady Attia
Appl. Sci. 2022, 12(7), 3675; https://doi.org/10.3390/app12073675 - 6 Apr 2022
Cited by 3 | Viewed by 6484
Abstract
To reduce crime rates, there is a need to understand and analyse emerging patterns of criminal activities. This study examines the occurrence patterns of crimes using the crime dataset of Lahore, a metropolitan city in Pakistan. The main aim is to facilitate crime [...] Read more.
To reduce crime rates, there is a need to understand and analyse emerging patterns of criminal activities. This study examines the occurrence patterns of crimes using the crime dataset of Lahore, a metropolitan city in Pakistan. The main aim is to facilitate crime investigation and future risk analysis using visualization and unsupervised data mining techniques including clustering and association rule mining. The visualization of data helps to uncover trends present in the crime dataset. The K-modes clustering algorithm is used to perform the exploratory analysis and risk identification of similar criminal activities that can happen in a particular location. The Apriori algorithm is applied to mine frequent patterns of criminal activities that can happen on a particular day, time, and location in the future. The data were acquired from paper-based records of three police stationsin the Urdu language. The data were then translated into English and digitized for automatic analysis. The result helped identify similar crime-related activities that can happen in a particular location, the risk of potential criminal activities occurring on a specific day, time, and place in the future, and frequent crime patterns of different crime types. The proposed work can help the police department to detect crime events and situations and reduce crime incidents in the early stages by providing insights into criminal activity patterns. Full article
(This article belongs to the Special Issue Computational Sensing and Imaging)
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21 pages, 20356 KiB  
Article
Line Segment Matching Fusing Local Gradient Order and Non-Local Structure Information
by Weibo Cai, Jintao Cheng, Juncan Deng, Yubin Zhou, Hua Xiao, Jian Zhang and Kaiqing Luo
Appl. Sci. 2022, 12(1), 127; https://doi.org/10.3390/app12010127 - 23 Dec 2021
Cited by 1 | Viewed by 2850
Abstract
Line segment matching is essential for industrial applications such as scene reconstruction, pattern recognition, and VSLAM. To achieve good performance under the scene with illumination changes, we propose a line segment matching method fusing local gradient order and non-local structure information. This method [...] Read more.
Line segment matching is essential for industrial applications such as scene reconstruction, pattern recognition, and VSLAM. To achieve good performance under the scene with illumination changes, we propose a line segment matching method fusing local gradient order and non-local structure information. This method begins with intensity histogram multiple averaging being utilized for adaptive partitioning. After that, the line support region is divided into several sub-regions, and the whole image is divided into a few intervals. Then the sub-regions are encoded by local gradient order, and the intervals are encoded by non-local structure information of the relationship between the sampled points and the anchor points. Finally, two histograms of the encoded vectors are, respectively, normalized and cascaded. The proposed method was tested on the public datasets and compared with previous methods, which are the line-junction-line (LJL), the mean-standard deviation line descriptor (MSLD) and the line-point invariant (LPI). Experiments show that our approach has better performance than the representative methods in various scenes. Therefore, a tentative conclusion can be drawn that this method is robust and suitable for various illumination changes scenes. Full article
(This article belongs to the Special Issue Computational Sensing and Imaging)
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