Image fusion based on nonsubsampled contourlet transform for infrared and visible light image

Adu, Jianhua; Gan, Jiulin; Wang, Yan; Huang, Jian

doi:10.1016/j.infrared.2013.07.010

Cited by 110 publications

(54 citation statements)

References 30 publications

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“…When the temperature of objects is same, the objects would have the same bright and dark features in the image. For the infrared image, the object in the image has a homogeneous texture different from its surrounds, so its texture is generally consistent and looks more even [28], by contract the infrared intensity images have the salient low frequency feature. The infrared polarization image has the salient high frequency feature.…”

Section: The Description Of Image Featurementioning

confidence: 99%

See 1 more Smart Citation

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: The Description Of Image Featurementioning

confidence: 99%

“…Research shows that the human visual feature response function is closer to exponential law [29]. So in this paper the exponential distribution is used to determine the response function of visual features [28]. As Formula (6) and (7).…”

Section: Visual Brightness and Contrast Response Functionmentioning

confidence: 99%

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

Zhang¹,

Yang²,

Ji³

et al. 2017

dtetr

View full text Add to dashboard Cite

show abstract

“…The main novelty is an algorithm for the decision-level fusion of two types of imagery: one acquired in the visual and one acquired in the infrared electromagnetic spectrum. Very recently, a new method based on a nonsubsampled contourlet transform, has been proposed to fuse the infrared image and the visible light image [21]. Furthermore, an adaptively weighted infrared and visual image fusion algorithm has been developed based on the multiscale top-hat selection transform [22].…”

Section: Related Workmentioning

confidence: 99%

Selection of a Visible-Light vs. Thermal Infrared Sensor in Dynamic Environments Based on Confidence Measures

Serrano-Cuerda¹,

Fernández-Caballero

López

2014

Applied Sciences

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This paper introduces a confidence measure scheme in a bimodal camera setup for automatically selecting visible-light or a thermal infrared in response to natural environmental changes. The purpose of the setup is to robustly detect people in dynamic outdoor scenarios under very different conditions. For this purpose, two efficient segmentation algorithms, one dedicated to the visible-light spectrum and another one to the thermal infrared spectrum, are implemented. The segmentation algorithms are applied to five different video sequences recorded under very different environmental conditions. The results of the segmentation in both spectra allow one to establish the best-suited confidence interval thresholds and to validate the overall approach. Indeed, the confidence measures take linguistic values LOW , M EDIU M and HIGH, depending on the reliability of the results obtained in visible-light, as well as in thermal infrared video.

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“…The NSCT has initially been suggested by Da Cunha et al [4] in 2006. It represents fully shift-invariant, multiscale and multidirectional transform scheme, which have been successfully used in the image fusion [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Fusion of infrared and visible images based on nonsubsampled shearlet transform and block compressive sensing sampling

Hu¹,

Shi²

2017

Ukr. J. Phys. Opt.

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Abstract. Image fusion is aimed at combining source images of the same scene into a single fused image with more reliable and precise information, which is suitable for further image-processing tasks. Here we present a novel method for fusing infrared and visible images, which is based on nonsubsampled shearlet transform (NSST) and block compressive-sensing sampling (BCSS). Compressive-sensing approach is widely used in various fields and constitutes a basis of low-signal sampling compression method employed under conditions of sparsity of signals. We investigate the BCSS technique in image fusion and suggest a fusion method where the NSST is applied for decomposition and reconstruction, and two-measure forms of local region energy are presented for maximum-choosing fusion rules for the NSST subband coefficients. Our approach is tested on five standard datasets of infrared and visible images. The results are compared with those obtained using several common fusion methods. The experimental results illustrate validity and efficiency of our method. Both the efficiency and robustness of the fusion are analyzed using a number of image-quality measures.

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Image fusion based on nonsubsampled contourlet transform for infrared and visible light image

Cited by 110 publications

References 30 publications

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

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

Selection of a Visible-Light vs. Thermal Infrared Sensor in Dynamic Environments Based on Confidence Measures

Fusion of infrared and visible images based on nonsubsampled shearlet transform and block compressive sensing sampling

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