Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (4): 1436-1442.doi: 10.13229/j.cnki.jdxbgxb.20241379

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Dynamic complementary compressive imaging method based on push-sweep mode

Chang-jun ZHA(),Kai-xing TAO,Yue LIU,Mao-yu ZHAO(),Hai-yan DONG   

  1. College of Advanced Manufacturing Engineering,Hefei University,Hefei 230601,China
  • Received:2024-12-30 Online:2025-04-01 Published:2025-06-19
  • Contact: Mao-yu ZHAO E-mail:zhachangjun@hfuu.edu.cn;chhmyzhao@126.com

Abstract:

When a traditional single-pixel compressive imaging system obtains measurement values, if the relative placement of the foreground target and imaging system is not static, the reconstructed image is blurred or completely distorted. To solve this problem, a dynamic complementary compressive imaging method based on a complementary mode is proposed. In this method, a single-column digital micro-mirror device is used to modulate the foreground image, two independent single-pixel sensors are used to obtain two optical signals reflected by the digital micro-mirror device, and the compressive measurement values of the foreground target image are obtained column by column, using the recovery mode of the dynamic compressive imaging is obtained;then based on this recovery mode, the traditional algorithm reconstructs the target image. In contrast to the results of traditional reconstruction, the results of each optical channel can be used to reconstruct two target images simultaneously. To improve the quality of the reconstruct image, this paper presents a quality enhancement method based on multi-channel image fusion. The results of simulation experiments show that the proposed dynamic complementary compressive imaging system not only can effectively reconstruct the foreground image, but the quality of the output image is not affected when the moving speed of the system changes within a certain range, demonstrating the good robustness of the system.

Key words: compressive sensing, dynamic compressive imaging, image quality enhancement, overlapping average algorithm

CLC Number: 

  • TP751.1

Fig.1

Schematic diagram of dynamic complementary compressive imaging system"

Fig.2

Traditional dynamic image model (Black grid indicates target image and red gridindicates single-column DMD)"

Fig.3

Schematic diagram of image column-by-column reconstruction"

Fig.4

Multi-channel image fusion"

Fig.5

Schematic diagram of vector alignment processing"

Fig.6

Reconstructed images of target image"

Fig.7

Relationship between PSNR and p"

Fig.8

Comparison of PSNR before and afterimage fusion"

Table 1

Comparison of PSNR for reconstructed images"

图像

大小

成像方法PSNR/dB
M=70M=80M=90M=100

128×

128

传统成像方法17.631 617.714 518.138 519.091 3
光路1的上部17.880 318.224 219.282 820.145 4
光路1的下部15.944 616.024 116.798 218.177 4
光路2的上部17.906 118.231 619.556 820.305 7
光路2的下部15.639 916.180 217.089 518.281 5
图像融合19.506 119.958 521.111 622.169 5
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