scholarly journals An Image Restoration Method Using Matrix Transform and Gaussian Mixture Model for Radio Tomographic Imaging

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Fei Gao ◽  
Cheng Sun ◽  
Heng Liu ◽  
Jianping An ◽  
Shengxin Xu
Keyword(s):  
Imaging System ◽  
Gaussian Mixture ◽  
Tomographic Imaging ◽  
System Model ◽  
Wireless Sensor ◽  
Target Image ◽  
Estimation Model ◽  
Rti Model ◽  
Restoration Method ◽  
Radio Tomographic Imaging

Radio Tomographic Imaging (RTI) is an attractive technique for imaging the nonmetallic targets within wireless sensor network. RTI has been used in many challenging environments and situations. Due to the accuracy of Radio Tomographic Imaging system model and the interference between the wireless signals of sensors, the image obtained from the RTI system is a degraded target image, which cannot offer sufficient details to distinguish different targets. In this paper, we treat the RTI system as an image degraded process, and we propose an estimation model based on mixture Gaussian distribution to derive the degradation function from the shadowing-based RTI model. Then we use this degradation function to recover an original image by a method called constrained least squares filtering. So far, many imaging models have been proposed for localization; however, they do not have a satisfied imaging accuracy. Simulated and experimental results show that the imaging accuracy of our proposed method is improved, and the proposed method can be used in the real-time circumstances.

scholarly journals ARTI (Adaptive Radio Tomographic Imaging): One New Adaptive Elliptical Weighting Model Combining with Tracking Estimates

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1034 ◽  
Author(s):  
Chunhua Zhu ◽  
Jiaojiao Wang ◽  
Yue Chen
Keyword(s):  
Imaging System ◽  
Tomographic Imaging ◽  
Measurement Noise ◽  
Moving Target ◽  
Indoor Environments ◽  
Smoothing Filter ◽  
Model Combining ◽  
Proposed Model ◽  
Radio Tomographic Imaging ◽  
Weight Model

Imaging and tracking performance suffers from the mismatch between the model and the measurements in an adaptive radio tomographic imaging system. In this paper, a model-based approach is reviewed and a new adaptive elliptical weighting model is proposed, in which the coverage of ellipse and the voxels weightings can adaptively match the actual environments, and the Savitzky–Golay smoothing filter is presented to eliminate the influence of measurement noise and multipath interference. In our proposed model, the optimal coverage of ellipse and weightings can be obtained from voxel weightings distribution inside the ellipse and pseudo-position area and trailing phenomenon. Finally, the development efforts are evaluated and validated with real experiments conducted in indoor environments for a moving target. The results have shown that the proposed algorithm can improve the accuracy of image and location estimates compared with the normalized weight model and the const-eccentricity weight model.

ARTI: An Adaptive Radio Tomographic Imaging System

2017 ◽  
Vol 66 (8) ◽  
pp. 7302-7316 ◽  
Author(s):  
Ossi Kaltiokallio ◽  
Riku Jantti ◽  
Neal Patwari
Keyword(s):  
Imaging System ◽  
Tomographic Imaging ◽  
Radio Tomographic Imaging

scholarly journals Modeling and image quality enhancement for dynamic compressive imaging system

2021 ◽  
Vol 15 ◽  
pp. 174830262110080
Author(s):  
Changjun Zha* ◽  
Qian Zhang* ◽  
Huimin Duan
Keyword(s):  
Image Quality ◽  
Imaging System ◽  
Quality Enhancement ◽  
Target Image ◽  
Compressive Imaging ◽  
Image Quality Enhancement ◽  
System A ◽  
Single Column ◽  
The Impact

Traditional single-pixel imaging systems are aimed mainly at relatively static or slowly changing targets. When there is relative motion between the imaging system and the target, sizable deviations between the measurement values and the real values can occur and result in poor image quality of the reconstructed target. To solve this problem, a novel dynamic compressive imaging system is proposed. In this system, a single-column digital micro-mirror device is used to modulate the target image, and the compressive measurement values are obtained for each column of the image. Based on analysis of the measurement values, a new recovery model of dynamic compressive imaging is given. Differing from traditional reconstruction results, the measurement values of any column of vectors in the target image can be used to reconstruct the vectors of two adjacent columns at the same time. Contingent upon characteristics of the results, a method of image quality enhancement based on an overlapping average algorithm is proposed. Simulation experiments and analysis show that the proposed dynamic compressive imaging can effectively reconstruct the target image; and that when the moving speed of the system changes within a certain range, the system reconstructs a better original image. The system overcomes the impact of dynamically changing speeds, and affords significantly better performance than traditional compressive imaging.

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