Multi-focus Image Fusion for Visual Sensor Networks in Wavelet Domain

Nooshyar, Mehdi; Abdipour, Mohammad; Khajuee, Mehdi

doi:10.1007/978-3-319-10849-0_3

Cited by 2 publications

(2 citation statements)

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“…Consequently the scale S B1 is obtained from S B2 and from the gravity factor M, and has the minimum value of three. Therefore, the following pairs are obtained and used in the experiment: (3,1), (4,2), (6,3), (8,4), (10,5), and (12,6). Structural elements are all square flat ones.…”

Section: Discussion On the Scale Of Elementsmentioning

confidence: 99%

“…[1][2][3][4] Since multi-scale transforms (MSTs in short) have features of multiple resolutions and time-frequency localization, they have always been the hot topics in the field of image fusion for infrared and visible images with strong complementarity. [5,6] The most widely used multi-scale trans-forms include Laplace pyramid, [7] discrete wavelet transform (DWT), [8][9][10][11] dual-tree complex wavelet transform (DTCWT), [12][13][14] contourlet transform (CT), [15] and nonsubsampled contourlet transform (NSCT). [16][17][18] The fusion process can usually be divided into three steps.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An infrared and visible image fusion method based upon multi-scale and top-hat transforms

He¹,

Zhang²,

Ji³

et al. 2018

Chinese Phys. B

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The high-frequency components in the traditional multi-scale transform method are approximately sparse, which can represent different information of the details. But in the low-frequency component, the coefficients around the zero value are very few, so we cannot sparsely represent low-frequency image information. The low-frequency component contains the main energy of the image and depicts the profile of the image. Direct fusion of the low-frequency component will not be conducive to obtain highly accurate fusion result. Therefore, this paper presents an infrared and visible image fusion method combining the multi-scale and top-hat transforms. On one hand, the new top-hat-transform can effectively extract the salient features of the low-frequency component. On the other hand, the multi-scale transform can extract highfrequency detailed information in multiple scales and from diverse directions. The combination of the two methods is conducive to the acquisition of more characteristics and more accurate fusion results. Among them, for the low-frequency component, a new type of top-hat transform is used to extract low-frequency features, and then different fusion rules are applied to fuse the low-frequency features and low-frequency background; for high-frequency components, the product of characteristics method is used to integrate the detailed information in high-frequency. Experimental results show that the proposed algorithm can obtain more detailed information and clearer infrared target fusion results than the traditional multiscale transform methods. Compared with the state-of-the-art fusion methods based on sparse representation, the proposed algorithm is simple and efficacious, and the time consumption is significantly reduced.

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