Convergent Three-dimensional Target Matching Filtering for Ghost Imaging

doi:10.12068/j.issn.1005-3026.2023.02.005

Abstract

Abstract: Aiming at the problems of low sampling frequency， low resolution and high noise in video ghost imaging reconstruction， an aggregated three-dimensional target matching filtering method was proposed. Firstly， each frame of the ghost imaging video is block-matched with the frame to be optimized and each frame of images after block matching is arranged to form a three-dimensional matrix according to the time sequence. According to the frame sequence of each frame picture， different frame weights are assigned to it. Then the matrix is subjected to three-dimensional weighted median filtering. After under-sampling simulation and experimental comparison of moving targets， the method proposed has not only lower noise figure and better structure retention， but also a better subjective evaluation compared with the existing three-dimensional filtering methods. Compared with the original experimental restoration graph， the proposed method reduces the noise figure and edge ambiguity by 34.25% and 6.86%， respectively， and the Brisque subjective evaluation was improved by 45.84%.

Key words: computational ghost imaging; block matching algorithm; median filter; three-dimensional weighted filtering; video filtering

CLC Number:

TN919.81

ZHANG Shun-yao， SANG Ai-jun， SONG Li-jun， WANG Shi-gang. Convergent Three-dimensional Target Matching Filtering for Ghost Imaging[J]. Journal of Northeastern University(Natural Science), 2023, 44(2): 186-191.

References

[1]Strekalov D V，Sergienko A V，Klyshko D N，et al.Observation of two-photon “ghost” interference and diffraction［J］.Physical Review Letters，1995，74(18):3600-3612.
[2]Gatti A，Brambilla E，Bache M，et al.Ghost imaging with thermal light:comparing entanglement and classical correlation［J］.Physical Review Letters，2004，93(9):093602.
[3]Shapiro J H.Computational ghost imaging［J］.Physical Review A，2008，78(6):061802.
[4]Koktzoglou I，Sheehan J J，Dunkle E E，et al.Highly accelerated contrast‐enhanced MR angiography:improved reconstruction accuracy and reduced noise amplification with complex subtraction［J］.Magnetic Resonance in Medicine，2010，64(6):1843-1848.
[5]韩申生，龚文林，陈明亮，等.基于稀疏和冗余表象的鬼成像雷达研究进展［J］.红外与激光工程，2015，44(9):2547-2555.(Han Shen-sheng，Gong Wen-lin，Chen Ming-liang，et al.Research progress of GISC lidar［J］.Infrared and Laser Engineering，2015，44(9):2547-2555.)
[6]曹非，赵生姝.基于计算鬼成像的双密钥光学加密方案［J］.光学学报，2017，37(1):130-137.(Cao Fei，Zhao Sheng-shu.Optical encryption scheme with double secret keys based on computational ghost imaging［J］.Acta Optica Sinica，2017，37(1):130-137.)
[7]唐晓燕，高昆，倪国强.量子光谱成像技术及其在对地观测中的应用前景［J］.航天返回与遥感，2011，32(5):80-88.(Tang Xiao-yan，Gao Kun，Ni Guo-qiang.Quantum spectral imaging technology and its prospect in earth observation application［J］.Spacecraft Recovery & Remote Sensing，2011，32(5):80-88.)
[8]Xu Z H，Chen W，Penuelas J，et al.1000 f/s computational ghost imaging using LED-based structured illumination［J］.Optics Express，2018，26(3):2427-2434.
[9]Nie X，Yang F，Liu X，et al.Noise-robust computational ghost imaging with pink noise speckle patterns［J］.Physical Review A，2021，104(1):013513.
[10]Yang Z，Li W，Song Z，et al.Tracking compensation in computational ghost imaging of moving objects［J］.IEEE Sensors Journal，2020，21(1):85-91.
[11]陶勇，王肖霞，杨风暴.基于高通滤波鬼成像的边缘检测方法［J］.激光与光电子学进展，2020，57(2):193-198.(Tao Yong，Wang Xiao-xia，Yang Feng-bao.Edge detection based on high-pass filter ghost imaging［J］.Laser & Optoelectronics Progress，2020，57(2):193-198.)
[12]毕厚杰，王健.新一代视频压缩编码标准——H.264/AVC［M］.北京:人民邮电出版社，2009.)(Bi Hou-jie，Wang Jian.New generation video compression coding standard—H.264/AVC［M］.Beijing:Posts & Telecom Press，2009.)
[13]付萍，陈贺新，付宁阳.全方位钟形加权中值滤波器［J］.电子科学学刊，1996，18(4):354-361.(Fu Ping，Chen He-xin，Fu Ning-yang.Omnidirectional bell-shaped weighted median filters［J］.Journal of Electronics & Information Technology，1996，18(4):354-361.)
[14]Dabov K，Foi A，Katkovnik V，et al.Image denoising by sparse 3-D transform-domain collaborative filtering［J］.IEEE Transactions on Image Processing，2007，16(8):2080-2095.
[15]Maggioni M，Boracchi G，Foi A，et al.Video denoising，deblocking，and enhancement through separable 4-D nonlocal spatiotemporal transforms［J］.IEEE Transactions on Image Processing，2012，21(9):3952-3966.
[16]谢小甫，周进，吴钦章.一种针对图像模糊的无参考质量评价指标［J］.计算机应用，2010，30(4):921-924.(Xie Xiao-fu，Zhou Jin，Wu Qin-Zhang.No-reference quality index for image blur［J］.Journal of Computer Applications，2010，30(4):921-924.)
[17]Choi M G，Jung J H，Jeon J W.No-reference image quality assessment using blur and noise［J］.International Journal of Computer Science and Engineering，2009，3(2):76-80.
[18]Mittal A，Moorthy A K，Bovik A C.No-reference image quality assessment in the spatial domain［J］.IEEE Transactions on Image Processing，2012，21(12):4695-4708.