An infrared polarization image fusion algorithm based on oriented Laplacian pyramid

Yue, Zhen; Li, Fanming

doi:10.1117/12.2054074

Cited by 6 publications

(3 citation statements)

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“…The infrared polarization and intensity images are obtained by our long infrared polarization imaging system in figure 5 and figure 6, the infrared polarization and intensity images are derived from the literature [21,32,33] in the Fig.7 ,Fig.8 and Fig.9.The infrared polarization imaging is related to the material, incident angle and the shape of object, the objects of same or different material have the different polarization effect, the infrared polarization images mainly reflect the edge and texture features. The infrared intensity images mainly reflect the brightness and outline features.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…For comparison purposes, the fusion is also performed using the NSCT, NSST, CDST, and MSBT(Multi-scale bilateral filter transform) method. The low frequency coefficients are fused by the local adaptive weighted energy, and the high frequency coefficients are fused by the absolute maximum choosing, which is adopted by the most papers [30][31][32].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…17and 18. To overcome the faults of anisotropic, shift-variant, and limited dictionary, the direction of the pyramid, support transformation, NSCT, and NSST [19][20][21][22] are used in infrared intensity and polarized image fusion, the fused image has better visual effect.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Infrared Polarization and Intensity Image Fusion Algorithm Based on Visual Brightness and Contrast Response Function

Zhang¹,

Yang²,

Ji³

et al. 2017

dtetr

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Abstract. In this paper, a novel fusion algorithm is proposed based on visual brightness and contrast response function, and it can better retain the difference features between infrared and polarization images to improve the visual effect of fused image. Firstly, the source images are enhanced by fractional differentia operator to improve the edge, texture, and brightness. Second, the low frequency and high frequency features of enhanced infrared polarization and intensity image are described by local mean and local skewness. Thirdly, the visual brightness and contrast response function are constructed by using local mean and local skewness based on human visual model. Next, the source images are fused by using the visual brightness and contrast response function, and then two kinds of fused images that are respectively are the low frequency feature fused image and detail feature fused image are gotten. Finally, the fused image is gotten by superposition of the low frequency feature fused image and detail feature fused image. The experiment shows that the result obtained by proposed algorithm performs better in both subjective and objective qualities.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Experimental Results and Analysismentioning

confidence: 99%