Single Pixel Imaging using Compressive Sensing and Spatial Light Modulator
محورهای موضوعی : فصلنامه علمی پژوهشی سنجش از دور راداری و نوری و سیستم اطلاعات جغرافیاییMohammad Roueinfar 1 , Mahdi Salmanian 2 , Ali Aghakasiri 3 , Abbas Bashiri 4 , Saeed Babanezhad 5
1 - Department of Electrical Engineering, IHU, Tehran, Iran
2 - Department of Electrical Engineering, IHU, Tehran, Iran
3 - Ph.D. Student of Communication, Department of Electrical Engineering, Sharif University, Tehran, Iran
4 - Department of Electrical Engineering, IHU, Tehran, Iran
5 - Department of Electrical Engineering, IHU, Tehran, Iran
کلید واژه: Measurement, pattern, Compressive Sensing, Mask, Single Pixel Method, Spatial Light Modulator,
چکیده مقاله :
Conventional cameras based on an array of pixels (CCD or CMOS) are commonly used to capture a target image at a certain distance. In this type of camera, all pixels are used to create the image. For CCD-based cameras at other wavelengths, including infrared and terahertz, having all the pixels increases the cost of the camera. The aim of this study is to design and build an imaging setup using a single pixel method to reduce the cost of the camera and to reconstruct the target image using less data. We verify this method for visible band due to availability of visible light equipment that can be generalized this method to other wavelengths. We use a spatial light modulator (SLM) produces two-level optical masks with random distribution with 20 x 20 pixels and a size of 10 x 10 cm and illuminates the target at a repetition rate of 1 Hz. The reflection of each mask from the target captured by a CCD camera and then we average of all pixels of the CCD to equate it with a single-pixel detector. The target image is reconstructed using a compressive sensing algorithm. The process of reconstructing the target image is performed using a minimum number of masks. We use the two norms L1 and TV to retrieve the target image. The simulation results show norm TV is more successful in target image retrieval. Also, with increasing the number of masks, the success rate in retrieving the target image increases.
Conventional cameras based on an array of pixels (CCD or CMOS) are commonly used to capture a target image at a certain distance. In this type of camera, all pixels are used to create the image. For CCD-based cameras at other wavelengths, including infrared and terahertz, having all the pixels increases the cost of the camera. The aim of this study is to design and build an imaging setup using a single pixel method to reduce the cost of the camera and to reconstruct the target image using less data. We verify this method for visible band due to availability of visible light equipment that can be generalized this method to other wavelengths. We use a spatial light modulator (SLM) produces two-level optical masks with random distribution with 20 x 20 pixels and a size of 10 x 10 cm and illuminates the target at a repetition rate of 1 Hz. The reflection of each mask from the target captured by a CCD camera and then we average of all pixels of the CCD to equate it with a single-pixel detector. The target image is reconstructed using a compressive sensing algorithm. The process of reconstructing the target image is performed using a minimum number of masks. We use the two norms L1 and TV to retrieve the target image. The simulation results show norm TV is more successful in target image retrieval. Also, with increasing the number of masks, the success rate in retrieving the target image increases.
Baraniuk, R., & Steeghs, P. (2007). Compressive radar imaging. IEEE radar conference, 128–133.
Ben-Yosef, N., & Sirat, G. (1982). Real-time spatial filtering utilizing the piezoelectric-elasto-optic effect. Optica Acta: International Journal of Optics, 29(4), 419–423.
Becker, S., Bobin, J., & Candès, E. J. (2011). NESTA: A fast and accurate first-order method for sparse recovery. SIAM Journal on Imaging Sciences, 4(1), 1-39.
Candès, E. J., Romberg, J. K., & Tao, T. (2006). Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59(8), 1207–1223.
Chan, W. L., Charan, K., Takhar, D., Kelly, K, F., Baraniuk, R. G., & Mittleman, D. M. (2008). A single-pixel terahertz imaging system based on compressed sensing. Appl. Phys. Lett. 93(12).
Donoho, L. (2006). Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306.
Gibson,G. M., Sun,B., Edgar,M. P., Phillips,D. B., Hempler,N., Maker, G. T., Malcolm,G. P. A and Padge, M. J. (2017). Real-time imaging of methane gas leaks using a single-pixel camera. Opt. Express 25(4), 2998–3005.
Hadfield, R. H. (2009). Single-photon detectors for optical quantum information applications. Nature photonics, 3(12), 696–705.
Hayasaki, Y., & Sato, R. (2020). Single‑pixel camera with hole‑array disk. Optical Review, 252-257.
He, Y-H., Zhang, Ai-X., Li, M-F., Huang, Y-Y., Quan, B-G., Li, D-Z., Wu, L-A., & Chen, L-M. (2020). High-resolution sub-sampling incoherent x-ray imaging with a single-pixel detector. APL Photonics, 5, 056102-1:056102-7.
Holoeye. (2012). lc-2012-spatial-light-modulator. Retrieved from holoeye: /holoeye.com
Hornett, S. M., Stantchev, R. I., Vardaki, M. Z., Beckerleg, C., & Hendry, E. (2016). Subwavelength terahertz imaging of graphene photoconductivity. Nano Lett. 16(11), 7019–7024.
Howland, G. A., Dixon, P. B., & Howell, J. C. (2011). Hoton-counting compressive sensing laser radar for 3D imaging. Appl. Opt. 50(31), 5917–5920.
Huynh, N., Lucka, F., Zhang, E. Z., Betcke, M. M., Arridge, S. R., Beard, P. C., & Cox, B. T. (2019). Single-pixel camera photoacoustic tomography. Journal of Biomedical Optics, 24(12). doi:10.1117/1.JBO.24.12.121907
Jacques, S. L. (2013). Optical properties of biological tissues: a review. Physics in Medicine and Biology, 58(11), R37-61. doi:doi: 10.1088/0031-9155/58/11/R37.
Lee, B. (2008). Introduction to ±12 Degree Orthogonal Digital Micromirror Devices (DMDs). Retrieved from ti: www.ti.com
Leihong, Z., Zhixiang, B., Hualong, Y., Zhaorui, W., Kaimin, W., & Dawei, Z. (2021). Restoration of Single pixel imaging in atmospheric turbulence by Fourier filter and CGAN. Applied Physics B, 1-16.
Ma, J. (2009). Single-pixel remote sensing. IEEE Geoscience and Remote Sensing Letters, 6(2), 199–203.
Majumder, S., Gupta, S., & Dubey, S. (2020). Spectral imaging using compressive sensing-based single-pixel modality. Electronic letters. doi:10.1049/el.2020.0757
Phillips, D. B., Sun, M-J., Taylor, J. M., Edgar, M. P., Barnett, S. M., Gibson, J. M., & Padgett1, M. J. (2017). Adaptive foveated single-pixel imaging with dynamic super sampling. Applied Optics, 1-10.
Radwell,N., Johnson,S. D., Edgar,M. P., Higham,C. F., Murray-Smith,R and Padgett,M. J. (2019). Deep learning optimized single-pixel lidar. Appl. Phys. Lett. 115(23).
Rousset, F. (2017). Single-pixel imaging : Development and applications of adaptive methods. Lyon. Retrieved from https://tel.archives-ouvertes.fr/tel-02067934/document
Sen, P., Chen, B., Garg, G., Marschner, S. R., Horowitz, M., Levoy, M., & Lensch, H. P. A. (2005). Dual Photography. ACM Trans. Graph, 24(3), 745–755.
Studer, V., Bobin, J., Chahid, M., Mousavi, H. S., Candes, E., Dahan, M. (2012). Compressive fluorescence microscopy for biological and hyperspectral imaging. Proc Natl Acad Sci U S A, 109(26), E1679-87. doi:DOI: 10.1073/pnas.1119511109
Sun, B., Edgarr, M. P., Bowmanl, R., Vitterts, L. E., Welsh, S., Bowmanan, A., & Padgett, M. J. (2013). 3D Computational Imaging with Single-Pixel Detectors. Science, 844-847.
Takhar, D., Laska, J. N., Wakin, M. B., Duarte, M. F., Baron, D., Sarvotham, S., Kelly, K. F., & Baraniuk, R. G. (2006). A new compressive imaging camera architecture using optical-domain compression. In: in Proc. of Computational Imaging IV at SPIE Electronic Imaging, (pp. 43-52).
Tao, T., Candès, E. J. (2006). Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Trans. Inf. Theory, 52(12), 5406–5425.
van den Berg, E., & Friedlander, M. P. (2008). Probing the Pareto frontier for basis pursuit solutions. SIAM J. on Scientific Computing, 31(2), 890-912.
Wakin, M. B., Laska, J. N., Duarte, M. F., Baron, D., Sarvotham, S., Takhar, D., Kelly, K. F., & Baraniuk, R. G. (2006). An architecture for compressive imaging. International Conference on Image Processing, (pp. 1273–1276).
Watts, C. M., Nadel, C. C., Montoya, J., Krishna, S., & Padilla, W. J. (2016). Frequency-division-multiplexed single-pixel imaging with metamaterials. Optica, 133-138.
Yu,W-K., Liu, X-F., Yao, X-R., Wang, C., Zhai, Y., & Zhai, G-J. (2014). Complementary compressive imaging for the telescopic system. Scientific Reports. doi:10.1038/srep05834
