Abstract
We propose a novel multi-spectral imaging method based on compressive sensing (CS). In CS theory, the enhancement of signal sparsity is important for accurate signal reconstruction. The main novelty of the proposed method is the employment of a self-correlation of an image, that is a local intensity similarity and multi-spectral correlation, to enhance the sparsity of the multi-spectral image to be recovered. Local intensity similarity, which is based on the concept that spatial changes in intensity are likely to be similar within local regions, contributes to sparsity enhancement. Furthermore, we exploit multi-spectral correlation to improve the sparsity of the multi-spectral components to be recovered. In order to simultaneously exploit different types of characteristics (i.e., local intensity similarity and multi-spectral correlation) for representing a signal as sufficiently sparse, we introduce a hierarchical joint sparsity model in the CS image recovery process. Our experiments show that the use of a self-correlation significantly improves the performance of multi-spectral image reconstruction.
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References
Arguello, H., Rueda, H., Wu, Y., Prather, D.W., Arce, G.R.: Higher-order computational model for coded aperture spectral imaging. Appl. Opt. 52(10), D12–D21 (2013)
Arguello, H., Mejia, Y., Arce, G.: Colored coded apertures optimization in compressive spectral imaging by restricted isometry property. In: Proceedings of the IEEE International Conferene on Image Processing (ICIP), pp. 600–604 (2014)
Baraniuk, R.G.: Compressive sensing. IEEE Signal Process. Mag. 24(4), 118–120 (2007)
Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2, 183–202 (2009)
Candes, E.J., Romberg, J.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52, 489–509 (2006)
Cao, X., Du, H., Tong, X., Dai, Q., Lin, S.: A prism-mask system for multispectral video acquisition. IEEE Trans. Pattern Anal. Mach. Intell. 33, 2423–2435 (2011)
Deng, C., Lin, W., Lee, B., Lau, C.T.: Robust image compression based on compressive sensing. In: Proceedings of the IEEE International Conference on Multimedia Expo (ICME), pp. 462–467 (2010)
Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52, 1289–1306 (2006)
Duarte, M.F., Baraniuk, R.G.: Spectral compressive sensing. Appl. Comput. Harmon. Anal. 35(1), 111–129 (2013)
Duarte, M.F., Sarvotham, S., Baron, D., Wakin, M.B., Baraniuk, R.G.: Distributed compressed sensing of jointly sparse signals. In: Proceedings of the Asilomar Conference on Signals, Systems, and Computers, pp. 1537–1541 (2005)
Fowler, J.E.: Compressive pushbroom and whiskbroom sensing for hyperspectral remote-sensing imaging. In: Proceedings of the IEEE International Conference on Image Processing (ICIP), pp 684–688 (2014)
Gan, L.: Block compressed sensing of natural images. In: Proceedings of the IEEE International Conference on Digital Signal Processing (DSP), pp. 403–406 (2007)
Han, S., Sato, I., Okabe, T., Sato, Y.: Fast spectral reflectance recovery using DLP projector. Int. J. Comput. Vis. 110, 172–184 (2014)
Hardeberg, J.Y., Schmitt, F., Brettel, H.: Multispectral color image capture using a liquid crystal tunable filter. Opt. Eng. 41, 2532–2548 (2002)
Majumdar, A., Ward, R.K.: Compressive color imaging with group sparsity on analysis prior. In: Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 1337–1340 (2010)
Majumdar, A., Ward, R.K.: Non-convex group sparsity: application to color imaging. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 469–472 (2010)
Miyake, Y.: Introduction to Multispectral Imaging. University of Tokyo Press, Tokyo (2006)
Monno, Y., Tanaka, M., Okutomi, M.: Direct spatio-spectral datacube reconstruction from raw data using a spatially adaptive spatio-spectral basis. In: Proceedings of the IS&T/SPIE Electronic Imaging (EI), pp. 866003-1–866003-8 (2013)
Nagesh, P., Li, B.: Compressive imaging of color images. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1261–1264 (2009)
Park, J., Lee, M., Grossberg, M.D., Nayer, S.K.: Multispectral imaging using multiplexed illumination. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1–8 (2007)
Sadeghipoor, Z., Lu, Y.M., Susstrunk, S.: A novel compressive sensing approach to simultaneously acquire color and near-infrared images on a single sensor. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2013)
Takhar, D., Laska, J.N., Wakin, M.B., Duarte, M.F., Baron, D., Sarvotham, S., Kelly, K.F., Baraniuk, R.G.: A new compressive imaging camera architecture using optical-domain compression. In: Proceedings of the Computational Imaging IV at SPIE Electronic Imaging, pp. 43–52 (2006)
Wagadarikar, A., John, R., Willett, R., Brady, D.: Single disperser design for coded aperture snapshot spectral imaging. Appl. Opt. 47(10), B44–B51 (2008)
Willett, R., Duarte, M.F., Davenport, A., Baraniuk, R.G.: Sparsity and structure in hyperspectral imaging: sensing, reconstruction, and target detection. IEEE Signal Process. Mag. 31(1), 116–126 (2014)
Yamaguchi, M., Haneishi, H., Ohyama, N.: Beyond red–green–blue (RGB): spectrum-based color imaging technology. J. Imaging Sci. Technol. 52, 10201-1–10201-15 (2008)
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Sugimura, D., Tomabechi, M., Hosaka, T. et al. Compressive multi-spectral imaging using self-correlations of images based on hierarchical joint sparsity models. Machine Vision and Applications 27, 499–510 (2016). https://doi.org/10.1007/s00138-016-0761-y
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DOI: https://doi.org/10.1007/s00138-016-0761-y