基于推扫式的动态互补压缩成像方法

doi:10.13229/j.cnki.jdxbgxb.20241379

摘要/Abstract

摘要：

传统的单像素压缩成像系统在获取测量值时，要求目标与成像系统保持相对静止状态；否则，重构出的图像存在模糊或完全失真。针对这一问题，本文提出了一种基于推扫式的动态互补压缩成像方法。该方法利用单列数字微镜器件来调制前景图像，采用2个独立的单像素传感器分别获取数字微镜装置反射的两路光信号，按列获取前景目标图像的压缩测量值，并给出动态压缩成像的恢复模型；然后，基于该模型，采用传统的算法重构出目标图像。与传统的重构结果不同的是，每条光路能够同时重构出2个目标图像。为提高重构图像质量，本文给出了一种基于多通道图像融合的质量增强方法。实验仿真与结果分析表明：本文提出的动态互补压缩成像系统不仅能有效地重构出前景图像，而且系统移动速度在一定范围内变化时，并不影响输出的图像质量，具有良好的鲁棒性。

关键词: 压缩感知, 动态压缩成像, 图像质量增强, 叠加平均算法

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

中图分类号:

TP751.1

查长军,陶开星,刘悦,赵茂俞,董海燕. 基于推扫式的动态互补压缩成像方法[J]. 吉林大学学报(工学版), 2025, 55(4): 1436-1442.

Chang-jun ZHA,Kai-xing TAO,Yue LIU,Mao-yu ZHAO,Hai-yan DONG. Dynamic complementary compressive imaging method based on push-sweep mode[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(4): 1436-1442.

图/表 9

图1

图2

图3

图4

图5

图6

图7

图8

表1

参考文献 22

1	Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306.
2	Haupt J, Nowak R. Compressive Sampling for Signal Detection[C]∥IEEE International Conference on Acoustics, Speech and Signal Processing, Honolulu, USA, 2007: 1509-1512.
3	Candes E J, Wakin M B. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine, 2008, 25(2):21-30.
4	Duarte M F, Davenport M A, Takhar D, et al. Single-pixel imaging via compressive sampling[J]. IEEE Signal Processing Magazine, 2008, 25(2): 83-91.
5	Wang L, Li W, Huang Z, et al. A Nautilus bionic multi-information fusion compressed-sensing acoustic imaging device[J]. Cell Reports Physical Science, 2023, 4(12): 1-21.
6	Shimobaba T, Endo Y, Nishitsuji T, et al. Computational ghost imaging using deep learning[J]. Optics Communications, 2018, 413: 147-151.
7	Liu S, Hu X, Lin W Z, et al. Terahertz compressed sensing imaging based on line array detection[J]. Optics and Lasers in Engineering, 2023,168:No.107685.
8	Zhao Z, Yu Z, Qi H, et al. Redundant compressed single-pixel hyperspectral imaging system[J]. Optics Communications, 2023, 546: No.129797.
9	Zhu X T, Li Y, Zhang Z B, et al. Adaptive real-time single-pixel imaging[J]. Optics Letters, 2024, 49(4): 1065-1068.
10	Phillips D B, Sun M J, Taylor J M, et al. Adaptive foveated single-pixel imaging with dynamic supersampling[J]. Science Advances, 2017, 3(4):No.1601782.
11	Kravets V, Stern A. Video compressive sensing using russian dolls ordering of hadamard basis for multi-scale sampling of a scene in motion using a single pixel camera[J]. Computational Imaging III:International Society for Optics and Photonics, 2018:No.2304594.
12	Tong Q, JiangY, Wang H, et al. Image reconstruction of dynamic infrared single-pixel imaging system[J]. Optics Communications, 2018, 410: 35-39.
13	Jiao S M. Motion estimation and quality enhancement for a single image in dynamic single-pixel imaging[J]. Optics Express, 2019, 27(9): 12841-12854.
14	刘金华, 吴佳韵, 饶云波, 等.融合小波框架和低秩的动态磁共振图像重建新思路[J]. 电子测量与仪器学, 2024, 38(7): 55-63.
	Liu Jin-hua, Wu Jia-yun, Rao Yun-bo, et al. New method for dynamic magnetic resonance image reconstruction combining wavelet frame and low-rank[J]. Journal of Electronic Measurement and Instrumentation, 2024, 38(7): 55-63.
15	杨春玲, 梁梓文. 静态与动态域先验增强的两阶段视频压缩感知重构网络[J]. 电子与信息学报, 2024, 46(11): 4247-4258.
	Yang Chun-ling, Liang Zi-wen. Static and dynamic-domain prior enhancement two-stage video compressed sensing reconstruction network[J]. Journal of Electronics and Information Technology, 2024, 46(11): 4247-4258.
16	Quach K G, Duong C N, Luu K, et al. Non-convex online robust PCA: Enhance sparsity via ℓ_p-norm minimization[J]. Computer Vision and Image Understanding, 2017, 158:126-140.
17	陈平平, 陈家辉, 王宣达, 等. Dice系数前向预测的快速正交正则回溯匹配追踪算法[J]. 电子与信息学报, 2024, 46(4): 1488-1498.
	Chen Ping-ping, Chen Jia-hui, Wang Xuan-da, et al. Regular backtracking fast orthogonal matching pursuit algorithm based on dice coefficient forward prediction[J]. Journal of Electronics and Information Technology, 2024, 46(4): 1488-1498.
18	Needell D, Vershynin R. Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit[J]. Journal of Selected Topics in Signal Processing, 2010, 4(2): 310-316.
19	Li Y H, Wang X D, Wang Z, et al. Modeling and image motion analysis of parallel complementary compressive sensing imaging system[J]. Optics Communications, 2018,423:100-110.
20	Yu W, Liu X, Yao X, et al. Complementary compressive imaging for the telescopic system[J]. Scientific Reports, 2015, 4(1):1-6.
21	王玺, 梁文凯, 杨虹, 等. 权重化QR分解的正交匹配追踪算法硬件实现[J]. 电子学报, 2024, 52(5):1534-1542.
	Wang Xi, Liang Wen-kai, Yang Hong, et al. Hardware implementation of orthogonal matching pursuit algorithm for weighted QR decomposition[J]. Acta Electronica Sinica, 2024, 52(5): 1534-1542.
22	Shuo Z. The LabView implement of synchronization overlapping average algorithm to suppress noise[J]. Journal of North China Electric Power University, 2009, 36(4): 73-76.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

图像大小	成像方法	PSNR/dB
图像大小	成像方法	M=70	M=80	M=90	M=100
128× 128	传统成像方法	17.631 6	17.714 5	18.138 5	19.091 3
	光路1的上部	17.880 3	18.224 2	19.282 8	20.145 4
	光路1的下部	15.944 6	16.024 1	16.798 2	18.177 4
	光路2的上部	17.906 1	18.231 6	19.556 8	20.305 7
	光路2的下部	15.639 9	16.180 2	17.089 5	18.281 5
	图像融合	19.506 1	19.958 5	21.111 6	22.169 5

[1]	周求湛,冀泽宇,王聪,荣静. 基于在线压缩重构的非侵入式电力负荷监测[J]. 吉林大学学报(工学版), 2024, 54(6): 1796-1806.
[2]	窦慧晶,谢东旭,郭威,邢路阳. 基于改进的正交匹配跟踪算法的波达方向估计[J]. 吉林大学学报(工学版), 2024, 54(12): 3568-3576.
[3]	田金鹏,侯保军. 基于深度展开自注意力网络的压缩感知图像重构[J]. 吉林大学学报(工学版), 2024, 54(10): 3018-3026.
[4]	曾春艳,严康,王志锋,王正辉. 多尺度生成对抗网络下图像压缩感知重建算法[J]. 吉林大学学报(工学版), 2023, 53(10): 2923-2931.
[5]	刘洲洲,张倩昀,马新华,彭寒. 基于优化离散差分进化算法的压缩感知信号重构[J]. 吉林大学学报(工学版), 2021, 51(6): 2246-2252.
[6]	窦慧晶,丁钢,高佳,梁霄. 基于压缩感知理论的宽带信号波达方向估计[J]. 吉林大学学报(工学版), 2021, 51(6): 2237-2245.
[7]	金心宇,谢慕寒,孙斌. 基于半张量积压缩感知的粮情信息采集[J]. 吉林大学学报(工学版), 2021, 51(1): 379-385.
[8]	单泽彪,刘小松,史红伟,王春阳,石要武. 动态压缩感知波达方向跟踪算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1938-1944.
[9]	刘洲洲, 王福豹. 改进的离散混合蛙跳算法压缩感知信号重构及应用[J]. 吉林大学学报(工学版), 2016, 46(4): 1261-1268.
[10]	王新华, 欧阳继红, 庞武斌. 压缩编码孔径红外成像超分辨重建[J]. 吉林大学学报(工学版), 2016, 46(4): 1239-1245.
[11]	于华楠, 代芳琳, 苏天恺. 基于压缩感知的三相电能质量扰动信号压缩及分类新方法[J]. 吉林大学学报(工学版), 2016, 46(3): 964-971.
[12]	张轶, 达新宇, 褚振勇. 低密度奇偶校验码的压缩感知重构[J]. 吉林大学学报(工学版), 2015, 45(3): 985-990.
[13]	王宏志，王贤龙，周婷婷. 基于光滑0范数的图像分块压缩感知恢复算法[J]. 吉林大学学报(工学版), 2015, 45(1): 322-327.
[14]	田文飚, 芮国胜, 张海波, 王林. 下非均匀信息采集及重构[J]. 吉林大学学报(工学版), 2014, 44(4): 1209-1214.
[15]	贺岩, 赵晓晖. 理论的宽带多用户认知系统合作检测[J]. 吉林大学学报(工学版), 2014, 44(4): 1165-1170.