N. Radha,T. Ranga Babu,



Extended spatial frequency,focus measures,image fusion,waveletbased focus measure,


The Multifocus image fusion objective in visual sensor networks is to combine the multi-focused images of the same scene into a focused fused image with improved reliability and interpretation. However, the existing fusion methods based on focus measures are not able to get entire focused fused image since they neglect the diagonal neighbor pixels during the selection of the focused objects. In order to get an image with all objects in focus a novel image fusion method using extended spatial frequency and wavelet based focus measures in the stationary wavelet transform domain is proposed. In our method, initially the two multi-focus source images are transformed and decomposed as low and high-frequency sub bands by using stationary wavelet transform. Then, each sub band is divided into equal subblocks. Focused sub-blocks of low and high-frequency sub bands are selected by using the extended spatial frequency and wavelet based focus measures. Lastly, the fused image is restored by performing the inverse stationary wavelet transform on selected sub-blocks. The performance of the proposed method is verified by carrying out the fusion on artificial, natural and misregistered multifocus images. The results of the proposed method are then compared with the results of existing image fusion methods. The experimental results indicate that proposed method not only removes artifacts in the fused image due to the shift-invariance of stationary wavelet transform and also preserves sharp details using extended spatial frequency and wavelet based focus measures.


I. Bhatnagar G, Raman B. A new image fusion technique based on directive
contrast. ELCVIA: electronic letters on computer vision and image analysis
II. Borwonwatanadelok P, Rattanapitak W, Udomhunsakul S. Multi-focus
image fusion based on stationary wavelet transform and extended spatial
frequency measurement. In: IEEE 2009 International Conference on
Electronic Computer Technology, 2009 pp. 77-81.
III. Cao L, Jin L, Tao H, Li G, Zhuang Z, Zhang Y. Multi-focus image fusion
based on spatial frequency in discrete cosine transform domain. IEEE signal
processing letters 2014; 22(2):220-224.
IV. Chen Y, Blum RS. A new automated quality assessment algorithm for
image fusion. Image and vision computing 2009; 27(10):1421-32.
V. Haghighat MB, Aghagolzadeh A, Seyedarabi H. Multi-focus image fusion
for visual sensor networks in DCT domain. Computers & Electrical
Engineering 2011; 37(5):789-97.
VI. Huang W, Jing Z. Evaluation of focus measures in multi-focus image
fusion. Pattern recognition letters 2007; 28(4):493-500.
VII. Kumar BS. Multifocus and multispectral image fusion based on pixel
significance using discrete cosine harmonic wavelet transform. Signal,
Image and Video Processing 2013; 7(6):1125-1143.
VIII. Li H, Wei S, Chai Y. Multifocus image fusion scheme based on feature
contrast in the lifting stationary wavelet domain. EURASIP Journal on
Advances in Signal Processing 2012; 2012(1):39.
IX. Li S, Kwok JT, Wang Y. Combination of images with diverse focuses using
the spatial frequency. Information fusion 2001; 2(3):169-176.
X. Li S, Yang B, Hu J. Performance comparison of different multi-resolution
transforms for image fusion. Information Fusion 2011; 12(2):74-84.
XI. Naidu VP. Image fusion technique using multi-resolution singular value
decomposition. Defence Science Journal 2011; 61(5):479-484.
XII. Nayar SK, Nakagawa Y. Shape from focus: An effective approach for
rough surfaces. In: IEEE 1990 Robotics and Automation International
Conference;Cincinnati,USA; 1990. pp. 218-225.
XIII. Paul S, Sevcenco IS, Agathoklis P. Multi-exposure and multi-focus image
fusion in gradient domain. Journal of Circuits, Systems and Computers
2016; 25(10):1650123.
XIV. Pertuz S, Puig D, Garcia MA. Analysis of focus measure operators for
shape-from-focus. Pattern Recognition 2013; 46(5):1415-1432.
XV. Petrovic VS, Xydeas CS. Gradient-based multiresolution image fusion.
IEEE Transactions on Image processing 2004; 13(2):228-237.

XVI. Pu T, Ni G. Contrast-based image fusion using the discrete wavelet
transform. Optical engineering 2000; 39(8):2075-2083.
XVII. Radha N, Babu TR. Performance evaluation of quarter shift dual tree
complex wavelet transform based multifocus image fusion using fusion
rules. International Journal of Electrical & Computer Engineering 2019;
9(2): 2377-2385.
XVIII. Sabre R, Wahyuni IS. Wavelet Decomposition in Laplacian Pyramid for
Image Fusion. International Journal of Signal Processing Systems 2016; 4
(1): pp.37-44.
XIX. Sahoo T, Mohanty S, Sahu S. Multi-focus image fusion using variance
based spatial domain and wavelet transform. In: IEEE 2011 International
Conference on Multimedia, Signal Processing and Communication
Technologies, 2011. pp. 48-51.
XX. Sharma EA, Gulati T. Performance Analysis of Unsupervised Change
Detection Methods for Remotely Sensed Images. International Journal of
Computational Intelligence Research 2017; 13(4):503-508.
XXI. Subbarao M, Tyan JK. Selecting the optimal focus measure for
autofocusing and depth-from-focus. IEEE transactions on pattern analysis
and machine intelligence 1998; 20(8):864-870.
XXII. Thelen A, Frey S, Hirsch S, Hering P. Improvements in shape-from-focus
for holographic reconstructions with regard to focus operators,
neighborhood-size, and height value interpolation. IEEE Transactions on
Image Processing 2008; 18(1):151-157.
XXIII. Vadhi R, Kilari V, Samayamantula S. Uniform based approach for image
fusion. In: Springer 2012 International Conference on Eco-friendly
Computing and Communication Systems; Berlin, Heidelberg; 2012. pp.
XXIV. Wang WW, Shui PL, Song GX. Multifocus image fusion in wavelet
domain. In: IEEE 2003 Machine Learning and Cybernetics International
Conference; Xi’an, China; 2003. pp. 2887-2890.
XXV. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment:
from error visibility to structural similarity. IEEE transactions on image
processing 2004; 13(4):600-12.
XXVI. Xie H, Rong W, Sun L. Wavelet-based focus measure and 3-d surface
reconstruction method for microscopy images. In: 2006 IEEE/RSJ
International Conference on Intelligent Robots and Systems, 2006. pp. 229-
XXVII. Xydeas CA, Petrovic V. Objective image fusion performance measure.
Electronics letters 2000; 36(4): 308-309.

XXVIII. Yang C, Zhang JQ, Wang XR, Liu X. A novel similarity based quality
metric for image fusion. Information Fusion 2008; 9(2):156-60.
XXIX. Yang J, Ma Y, Yao W, Lu WT. A Spatial domain and frequency domain
integrated approach to fusion multifocus images. The International Archives
of the Photogrammetry, Remote Sensing and Spatial Information Sciences
2008; 37(PART B7).
XXX. Yang Y, Zheng W, Huang S. Effective multifocus image fusion based on
HVS and BP neural network. The Scientific World Journal 2014.
XXXI. Zhang L, Zhang L, Mou X, Zhang D. FSIM: A feature similarity index for
image quality assessment. IEEE transactions on Image Processing 2011;
XXXII. Zheng Y, Essock EA, Hansen BC, Haun AM. A new metric based on
extended spatial frequency and its application to DWT based fusion
algorithms. Information Fusion 2007; 8(2):177-192.

View Download