Journal of Textile Research ›› 2019, Vol. 40 ›› Issue (06): 125-132.doi: 10.13475/j.fzxb.20180505008

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Nonwovens multi-focus fusion based on GHM multi-wavelet transform

CHEN Yang1, XIN Binjie2(), DENG Na1   

  1. 1. College of Electrical Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
    2. College of Fashion, Shanghai University of Engineering Science, Shanghai 201620, China
  • Received:2018-05-21 Revised:2019-02-21 Online:2019-06-15 Published:2019-06-25
  • Contact: XIN Binjie E-mail:xinbj@sues.edu.cn

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Abstract:

Aiming at the problem that the fibers in some areas of the fabric image captured by the optical microscope under a single focal plane will be blurred, a multi-focus image fusion algorithm based on multi-wavelet transform was proposed. Using the self-built nonwoven fabric microscopic imaging system to acquire the fabric image sequences under different focal planes, the initial image sequence was subjected to critical sampling pre-filtering, and the two fusion methods were used to process the high and low frequencies of the images one by one. The original fabric fusion image was after wavelet fusion and inverse transformation, the original fusion image and the subsequent single-focus surface image were merged according to the above method. After the integration cycle was completed, all the fiber regions could be clearly displayed until the convergence was completed. Experiments show that the fusion method can digitally image the image sequence taken under different focus planes, achieve the effect of clear focusing and fusion of the fiber mesh in the full field of view within a single image, and provide the foundation for the computer image processing and measurement laying algorithm.

Key words: critical sampling prefiltering, GHM multi-wavelet, multi-focus image fusion, nonwoven image, microscopic imaging

CLC Number: 

  • TP311.1

Fig.1

Image acquisition device"

Fig.2

Nonwoven fabric sample images captured at different focal planes. (a)Image 1; (b) Image 2; (c) Image 3; (d) Image 4; (e) Image 5; (f) Image 6; (g) Image 7; (h) Image 8; (i) Image 9"

Fig.3

Multi-focus image fusion algorithm flow chart"

Fig.4

Multi-focus image fusion effect of each method. (a)Multi-wavelet;(b)Single wavelet;(c)Double wavelet;(d)Partial enlargement of multi-wavelet;(e)Partial enlargement of single wavelet;(f)Partial enlargement of double wavelet;(g)Contrast pyramid;(h)Laplacian pyramid;(i)Gradient pyramid;(j)Partial enlargement of contranst pyramid;(k)Partical enlargement of laplacian pyramid;(l)Partical enlargement of gradient pyramid"

Fig.5

Comparison of original and fusion effect diagrams of 6 kinds of samples. (a) 1#; (b) 2#; (c) 3#; (d) 1# after fusion; (e) 2# after fusion;(f) 3# after fusion; (g) 4#; (h) 5#; (i) 6#; (j) 4# after fusion; (k) 5# after fusion; (l) 6# after fusion"

Tab.1

Fusion image quality assessment form"

类型 H- g? MI MMSE PSNR
多小波变换 6.618 6 8.011 7 0.532 2 44.755 0 31.876 0
加权平均 6.458 5 1.783 4 0.861 5 48.865 3 31.843 8
单小波变换 6.367 7 4.751 7 0.787 8 52.281 5 31.198 0
双小波变换 6.336 3 7.954 3 0.681 5 55.529 7 30.771 0
拉普拉斯金字塔融合 5.632 1 7.926 5 0.543 8 58.908 4 30.757 1
对比度金字塔融合 4.409 8 7.944 3 0.564 5 79.210 3 29.284 4
梯度金字塔融合 6.862 6 3.778 2 0.629 4 57.966 0 30.511 0
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