Hybrid fusion and interpolation algorithm with near-infrared image

doi:10.1007/s11704-014-4230-3

Front. Comput. Sci.

2015, Vol. 9

Issue (3) : 375-382 https://doi.org/10.1007/s11704-014-4230-3

RESEARCH ARTICLE

Hybrid fusion and interpolation algorithm with near-infrared image

Xiaoyan LUO^1,^*(

),Jun ZHANG²,Qionghai DAI³

1. Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China
2. National Key Laboratory of CNS/ATM, School of Electronics and Information Engineering, Beihang University, Beijing 100191, China
3. Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China

Download: PDF(663 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Silicon-based digital cameras can record visible and near-infrared (NIR) information, in which the full color visible image (RGB) must be restored from color filter array (CFA) interpolation. In this paper, we propose a unified framework for CFA interpolation and visible/NIR image combination. To obtain a high quality color image, the traditional color interpolation from raw CFA data is improved at each pixel, which is constrained by the corresponding monochromatic NIR image in gradient difference. The experiments indicate the effectiveness of this hybrid scheme to acquire joint color and NIR information in real-time, and show that this hybrid process can generate a better color image when compared to treating interpolation and fusion separately.

Keywords image fusion color filter array (CFA) interpolation demosaicing

Corresponding Author(s): Xiaoyan LUO

Issue Date: 18 May 2015

Cite this article:

Xiaoyan LUO,Jun ZHANG,Qionghai DAI. Hybrid fusion and interpolation algorithm with near-infrared image[J]. Front. Comput. Sci., 2015, 9(3): 375-382.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-014-4230-3
https://academic.hep.com.cn/fcs/EN/Y2015/V9/I3/375

1	Guarnera M, Messina G, Tomaselli V. Adaptive color demosaicing and false color removal. Journal of Electronic Imaging, 2010, 19(2): 1-16 https://doi.org/10.1117/1.3432486
2	Menon D, Andriani S, Calvagno G. Demosaicing with directional filtering and a posteriori decision. IEEE Transactions on Image Processing, 2007, 16(1): 132-141 https://doi.org/10.1109/TIP.2006.884928
3	Su C Y, Kao W C. Effective demosaicing using subband correlation. IEEE Transactions on Consumer Electronics, 2009, 55(1): 199-204 https://doi.org/10.1109/TCE.2009.4814435
4	Chung K H, Chan Y H. Low-complexity color demosaicing algorithm based on integrated gradients. Journal of Electronic Imaging, 2010, 19(2): 1-15 https://doi.org/10.1117/1.3432484
5	Itoh Y. Similarity-based demosaicing algorithm using unified highfrequency map. IEEE Transactions on Consumer Electronics, 2011, 57(2): 597-605 https://doi.org/10.1109/TCE.2011.5955197
6	Hirakawa K, Parks T W. Adaptive homogeneity-directed demosaicing algorithm. IEEE Transactions on Image Processing, 2005, 14(3): 360-369 https://doi.org/10.1109/TIP.2004.838691
7	Wu X, Zhang N. Primary-consistent soft-decision color demosaicking for digital cameras. IEEE Transactions on Image Processing, 2004, 13(9): 1263-1274 https://doi.org/10.1109/TIP.2004.832920
8	Gunturk B K, Altunbasak Y, Mersereau R M. Color plane interpolation using alternating projections. IEEE Transactions on Image P<?Pub Caret?>rocess, 2002, 11(9): 997-1013 https://doi.org/10.1109/TIP.2002.801121
9	Lian N, Chang L, Tan Y, Zagorodnov V. Adaptive filtering for color filter array demosaicking. IEEE Transactions on Image Processing, 2007,16(10): 2515-2525 https://doi.org/10.1109/TIP.2007.904459
10	Lu Y M, Karzand M, Vetterli M. Demosaicking by alternating projections: theory and fast one-step implementation. IEEE Transactions on Image Processing, 2010, 19(8): 2085-2098 https://doi.org/10.1109/TIP.2010.2045710
11	Bennett E P, Mason J L, McMillan L. Multispectral bilateral video fusion. IEEE Transactions on Image Processing, 2007, 16(5): 1185-1194 https://doi.org/10.1109/TIP.2007.894236
12	Zhang X, Sim T, Miao X. Enhancing photographs with near infrared images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2008, 1-8
13	Schaul L, Fredembach C, Süsstrunk S. Color image dehazing using near-infrared. In: Proceedings of the 16th IEEE International Conference on Image Processing. 2009, 1629-1632
14	Süsstrunk S, Fredembach C, Tamburrino D. Automatic skin enhancement with visible and near-infrared image fusion. In: Proceedings of the International Conference on Multimedia. 2010, 1693-1696 https://doi.org/10.1145/1873951.1874324
15	Fredembach C, Süsstrunk S. Illuminant estimation and detection using near-infrared. In: Proceedings of the International Society for Optics and Photonics Digital Photography. 2009
16	Salamati N, Fredembach C, Süsstrunk S. Material classification using color and NIR images. In: Proceedings of the 17th Color Imaging Conference Final Program and Proceedings. 2009, 216-227
17	Fredembach C, Süsstrunk S. Colouring the near-infrared. In: Proceedings of the 16th Color Imaging Conference Final Program and Proceedings. 2008, 176-182
18	Krishnan D, Fergus R. Dark flash photography. ACM Transactions on Graphics, 2009, 28(3), Artile No. 96
19	Matsui S, Okabe T, Shimano M, Sato Y. Image enhancement of lowlight scenes with near-infrared flash images. Lecture Notes in Computer Science, 2010, 5994: 213-223 https://doi.org/10.1007/978-3-642-12307-8_20
20	Hirakawa K, Parks TW. Joint demosaicing and denoising. IEEE Transactions on Image Processing, 2006, 15(8): 2146-2157 https://doi.org/10.1109/TIP.2006.875241
21	Donoho D L. Sparse components of images and optimal atomic decomposition. Constructive Approximation, 2001, 17(3): 353-382 https://doi.org/10.1007/s003650010032
22	Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306 https://doi.org/10.1109/TIT.2006.871582
23	Wang Y, Yang J, Yin W, Zhang Y. A new alternating minimization algorithm for total variation image reconstruction. SIAM Journal on Imaging Sciences, 2008, 1(3): 248-272 https://doi.org/10.1137/080724265
24	Lu Y M, Fredembach C, Vetterli M, Süsstrunk S. Designing color filter arrays for the joint capture of visible and near-infrared images. In: Proceedings of the 16th IEEE International Conference on Image Processing. 2009, 3797-3800 https://doi.org/10.1109/icip.2009.5414324
25	Smith P R. Bilinear interpolation of digital images. Ultramicroscopy, 1981, 6(2): 201-204 https://doi.org/10.1016/0304-3991(81)90061-9
26	Hamilton J F, Adams J E. Adaptive color plane interpolation in single sensor color electronic camera. US Patent, 5 629 734, 1997-<month>07</month>-<day>29</day>

[1]

Supplementary Material-Highlights in 3-page ppt

Download

[1]	Xuexia ZHONG,Guorui FENG,Jian WANG,Wenfei WANG,Wen SI. A novel adaptive image zooming scheme via weighted least-squares estimation[J]. Front. Comput. Sci., 2015, 9(5): 703-712.
[2]	Yin LU,Fuxiang WANG,Xiaoyan LUO,Feng LIU. Novel infrared and visible image fusion method based on independent component analysis[J]. Front. Comput. Sci., 2014, 8(2): 243-254.
[3]	Tang Yuanyan. Status of pattern recognition with wavelet analysis[J]. Front. Comput. Sci., 2008, 2(3): 268-294.
[4]	YANG Xuejun, WANG Panfeng, DU Yunfei, ZHOU Haifang. A data-distributed parallel algorithm for wavelet-based fusion of remote sensing images[J]. Front. Comput. Sci., 2007, 1(2): 231-240.

Viewed

Full text

Abstract

Cited

Shared

Discussed