Affiliations: [a] Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei 230039, Anhui, China | [b] Department of Electronics and Electrical Engineering, Hefei University, Hefei 230601, Anhui, China | E-mail: [email protected]
Correspondence:
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Corresponding author: Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei 230039, Anhui, China. E-mail: [email protected].
Abstract: The traditional compressive imaging method for a moving object has the problem of repeated compressive sampling when the object stops moving or remains stationary. To solve this problem, this paper presents an improved method for compressive imaging in this situation. In contrast to traditional methods, the image is preprocessed before reconstruction. First, the Euclidean distance between the current measurement vector and the previous one is calculated, and then a threshold is used to determine whether the new measurement vector is valid. If the distance is below the threshold, the measurement vector is a repetition of the previous one and is discarded; otherwise, the measurement vector is valid and the measurement value is retained and used for image reconstruction. Theoretical analysis and simulation results show that the proposed method can eliminate the repeated sampling problem caused by a pause in the movement of an object, enabling effective reconstruction of the moving object image.
