access icon free Fusion for visual context enhancement using intensity transformation function of infrared images

An intensity transformation function of infrared images is presented and used for context enhancement of visual images, upon which a new image fusion method in the shift-invariant wavelet domain is developed. The function behaves like a sigmoid function and shifts and expands the range of dark pixels of infrared images. These adjustments can avoid artificial bright pixels introduced in the later enhancing of visual images and the bleaching effect in the final fused images owing to the exponential map of very dark pixels of the infrared images. Experimental results validate the subjective performance of the proposed method, along with objective performance through several suggested quantitative metrics.

Inspec keywords: image resolution; functions; wavelet transforms; infrared imaging; image enhancement; image fusion

Other keywords: intensity transformation function; infrared images; visual context enhancement; image fusion method; Sigmoid function; shift-invariant wavelet domain; exponential map; bleaching effect

Subjects: Integral transforms; Computer vision and image processing techniques; Integral transforms; Sensor fusion; Image sensors; Optical, image and video signal processing

References

    1. 1)
      • 4. Shah, P., et al.: ‘Context enhancement to reveal a camouflaged target and to assist target localization by fusion of multispectral surveillance videos’, Signal Image Video Process., 2011, pp. 116.
    2. 2)
      • 2. Liu, Z., Laganiere, R.: ‘Context enhancement through infrared vision: a modified fusion scheme’, Signal Image Video Process., 2007, 1, (4), pp. 293301 (doi: 10.1007/s11760-007-0025-4).
    3. 3)
      • 6. Hossny, M., Nahavandi, S., Creighton, D.: ‘Comments on information measure for performance of image fusion’, Electron. Lett., 2008, 44, (18), pp. 10661067 (doi: 10.1049/el:20081754).
    4. 4)
      • 3. Liu, Z., et al.: ‘Objective assessment of multiresolution image fusion algorithms for context enhancement in night vision: a comparative survey’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (1), pp. 94109 (doi: 10.1109/TPAMI.2011.109).
    5. 5)
      • 5. Rockinger, O.: ‘Image sequence fusion using a shift-invariant wavelet transform’. Proc. IEEE Int. Conf. Image Processing, Santa Barbara, CA, October 1997, vol. 3, pp. 288291.
    6. 6)
      • 1. Tao, L., Asari, V.K.: ‘Adaptive and integrated neighbourhood dependent approach for nonlinear enhancement of color images’, J. Electron. Imaging, 2005, 14, (4), pp. 114 (doi: 10.1117/1.2136903).
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2013.0789
Loading

Related content

content/journals/10.1049/el.2013.0789
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading