Weighted Bilinear Interpolation Based Generic Multispectral Image Demosaicking Method

  • Medha Gupta Computer Science and Engineering, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, India
  • Mangey Ram Computer Science and Engineering, Department of Mathematics, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, India
Keywords: Multispectral Images, Demosaicking, Interpolation, Weighted Bilinear, Multispectral Filter Array (MSFA)

Abstract

Multispectral imaging systems acquire images having more than three spectral bands and these images play crucial role in various applications such as remote sensing ,medical imaging, military surveillance, vision inspection for food quality control, archaeological surveys etc. But the high cost of multispectral imaging systems limit their usage. Similar to the use of color-filter-array interpolation methods in development of low cost RGB color cameras, researchers have been exploring the use of multispectral image demosaicking technologies for developing affordable multispectral imaging systems. In this paper, we present a generic simple weighted bilinear interpolation based multispectral image demosaicking method. This method is applicable for any number of spectral bands image, however it critically depends upon the multispectral filter array that needs to be carefully designed for the weighted bilinear method to be easily applicable. We use two publically available multispectral image data sets for the performance evaluation of the proposed approach and present some interesting insights derived from the experimental results.

Downloads

Download data is not yet available.

References

Addesso, P., Longo, M., Montone, R., Restaino, R., & Vivone, G. (2017). Interpolation and combination rules for the temporal and spatial enhancement of SEVIRI and MODIS thermal image sequences. International Journal of Remote Sensing, 38(7), 1889-1911.

Aggarwal, H. K., & Majumdar, A. (2014, July). Compressive sensing multi-spectral demosaicing from single sensor architecture. In2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP)(pp. 334-338). IEEE.

Aggarwal, H.K., & Majumdar, A. (2015 January). Multi-spectral demosaicking: A joint-sparse elastic-net formulation. Eighth International Conference on Advances in Pattern Recognition (ICAPR) (pp. 1-5). Indian Statistical Institute, Kolkata, India.

Bayer, Bryce E. (1976). Color imaging array. U.S. Patent 3971065.

Brauers, J., & Aach, T. (2006). A color filter array based multispectral camera. Proceeding of Workshop Farbbildverarbeitung. German Color Group.

Brennera, C., Thiema, C.E., Wizemannb, H.D., Bernhardta, M., & Schulza, K. (2017). Estimating spatially distributed turbulent heat fluxes from high-resolution thermal imagery acquired with a UAV system. International Journal of Remote Sensing, 38(8), 3003-3026.

CAVE Projects: Multispectral Image Database. Available: http://www.cs.columbia.edu/CAVE/databases/multispectr/

Galidaki, G., Zianis, D., Gitas, I., Radoglou, K., Karathanassi, V., Tsakiri–Strati, Woodhouse, I., & Mallinis, G. (2017). Vegetation biomass estimation with remote sensing: focus on forest and other wooded land over the Mediterranean ecosystem. International Journal of Remote Sensing, 38(7), 1940-1966.

Goyal, P., Khanna, N., Dosad, J., & Gupta, M. (2014) Impact of neighborhood size onmedian filter based color filter array interpolation. Mathematics in Engineering, Science and Aerospace (MESA). 5(3), 265-274.

Jaiswal, S.P., Fang, L., Jakhetiya, V., Pang, J., Mueller, K., Au, O.C. (2016). Adaptive multispectral demosaicking based on frequency domain analysis of spectral correlation. IEEE Transactions on Image Processing. 26(2), 953-968.

Kalkan, H., Tekinay, C., & Yardimci, Y. (2010 September). Classification of multispectral satellite land cover data by 3D local discriminant bases algorithm. Proceeding of 25th International Symposium on Computer and Information Sciences (62, 237-240).

London, UK, Springer, Dordrecht.Li, X., Gunturk, B., & Zhang, L. (2008, January). Image demosaicing: A systematic survey. InVisual Communications and Image Processing 2008(Vol. 6822, p. 68221J). International Society for Optics and Photonics.

Losson, O., Macaire, L., & Yang, Y. (2010). Comparison of color demosaicing methods. In Advances in Imaging and Electron Physics(Vol. 162, pp. 173-265). Elsevier.

MacLachlan, A., Roberts, G., Biggs, E., & Boruff, B. (2017). Subpixel land-cover classification for improved urban area estimates using Landsat. International Journal of Remote Sensing, 38(20), 5763-5792.

Mangai, U.G., Samanta, S., Das, S., Chowdhury, P.R., Varghese, K., & Kalra, M. (2010 Nov). A hierarchical multi-classifier framework for land form segmentation using multi-spectral satellite images-A case study over the Indian subcontinent. Proceeding of IEEE Fourth Pacific-Rim Symposium on Image and Video Technology (PSIVT) (pp. 306-313).

Nanyang Technological University (NTU), Singapore. Miao, L., & Qi, H. (2006). The design and evaluation of a generic method for generating mosaicked multispectral filter arrays. IEEE Transactions on Image Processing. 15(9),2780-2791.

Miao, L., Qi, H., Ramanath, R., & Snyder, W.E. (2006). Binary tree-based generic demosaicking algorithm for multispectral filter arrays. IEEE Transactions on Image Processing. 15(11), 3550-3558.

Mihoubi, S., Losson, O., Mathon, B., & Macaire, L. (2015 November). Multispectral demosaicking using intensity-based spectral correlation. Proceeding of International Conference Image Processing Theory, Tools Applications (IPTA) (pp. 461466).IEEE.

Mizutani, J., Ogawa, S., Shinoda, K., Hasegawa, M., & Kato, S. (2014 December). Multispectral demosaicking algorithm based on interchannel correlation. Visual Communications and Image Processing Conference (pp. 474-477). IEEE,Valletta, Malta.

Monno, Y., Kikuchi, S., Tanaka, M., & Okutomi, M. (2015). A practicalone shot multispectral imaging system using a single image sensor. IEEE Transaction on Image Processing. 24(10), 30483059.

Monno, Y., Tanaka, M., & Okutomi, M., (2011 September). Multispectral demosaicking using adaptive kernel upsampling. Proceeding of 18th IEEE International Conference on Image Processing (pp. 3157-3160).

Brussels, Belgium.Monno, Y., Tanaka, M., & Okutomi, M. (2012 January). Multispectral demosaicking using guided filter. Proceeding of SPIE (pp. 82990O). 8299.

Pearce, A.K., Fuchs, A.V. Fletcher, N.L., & Thurecht, K.J. (2016). Targeting nanomedicines to prostate cancer: evaluation of specificity of ligands to two different receptors in vivo. Pharmaceutical Research. 33(10), 2388-2399. Popescu, A.C., & Farid, H. (2005). Exposing digital forgeries in color filter array interpolated images. IEEE Transactions Signal processing. 53(10), 3948–3959.

Shinoda, K., Ogawa, S., Yanagi, Y., Hasegawa, M., Kato, S., Ishikawa, M., Komagata, H., & Kobayashi, N., (2015 December). Multispectral filter array and demosaicking for pathological images. Proceeding of 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) (pp. 697-703). IEEE.

Hong Kong, China.Wang, Z., Bovik, A.C., Sheikh, H.R., & Simoncelli, E.P. (2004). Image quality assessment: From error visibility to structure similarity. IEEE Transaction on Image Processing. 13(4), 600-612.

Yamaguchi, M., Haneishi, H., Fukuda, Kishimoto, J. Kanazawa, Tsuchida, H. Iwama, R., & Ohyama, N., (2006 January). High-fidelity video and still-image communication based on spectral information: natural vision system and its applications. Proceeding of SPIE Spectral Imaging: Eighth International Symposium on Multispectral Color Science (pp. 129-140). San Jose, California, United States.

Published
2019-09-20
Section
Articles