An Effective Multi-focus Image Fusion Based on Law’s of Texture Enegery Measures in Integrated Wavelet Domain


  • Kiran S. Associate Professor and principal investigator, Department of CSE, YSR Engineering College of YVU, Proddatur, Kadapa, AP, India-516360


NSCT, DTCWT, LTEM, Texture, Image fusion


Feature extraction provides better description of given image. In computer vision, characterization of images is a challenging task. Due to intensity variations and non-uniformities. In such situations the texture description plays a vital role to extract essential information without any distortion, to attain such information texture energy measures are very useful. This paper concentrates on feature extraction methods by combining NSCT (Non-Subsampled Contourlet Transform) domain with the combination of DTCWT (Dual-Tree Complex Wavelet Transforms). Energy measures are applied in the form of masks to get more efficient features comparing with other approaches. A set of test images are considered to the energy measures which significantly improves correlation of texture for given test dataset. Different kinds of measures are considered like average gradient, edge intensity, gray mean value, standard deviation etc. to show the significance of proposed method with greater time complexity.


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Mathavan, S., Kumar, A., Kamal, K., Nieminen, M., Shah, H., & Rahman, M. (2016). Fast Segmentation of Industrial Quality Pavement Images using Laws Texture Energy Measures and k-Means Clustering. Journal of Electronic Imaging, 25(5), [053010].

Setiawan, Arden & Elysia, & Wesley, Julian & Purnama, Yudy. (2015). Mammogram Classification using Law's Texture Energy Measure and Neural Networks. Procedia Computer Science. 59. 92-97. 10.1016/j.procs.2015.07.341.

Kvyetnyy, Roman & Olga, Sofina & Olesenko, Alla & Komada, Pawel & Sikora, Jan & Kalizhanova, Aliya & Smailova, Saule. (2017). Method of image texture segmentation using Laws' energy measures. 1044561. 10.1117/12.2280891.

Dash, Sonali & Jena, Umaranjan. (2018). Multi-resolution Laws’ Masks based texture classification. Journal of Applied Research and Technology. 15. 10.1016/j.jart.2017.07.005.

P., Govindaraj & Sudhakar, M.S.. (2017). Shape characterization using laws of texture energy measures facilitating retrieval. The Imaging Science Journal. 66. 1-8. 10.1080/13682199.2017.1380356.

Kamal, Khurram & Qayyum, Rizwan & Mathavan, Senthan & Zafar, Tayyab. (2017). Wood defects classification using laws texture energy measures and supervised learning approach. Advanced Engineering Informatics. 34. 125-135. 10.1016/j.aei.2017.09.007.

Ganasala, Padma & Prasad, Dr. A.D.. (2020). Medical image fusion based on laws of texture energy measures in stationary wavelet transform domain. International Journal of Imaging Systems and Technology. 30. 10.1002/ima.22393.

Ganasala, Padma & Prasad, Dr. A.D.. (2020). Functional and Anatomical Image Fusion based on Texture Energy Measures in NSST Domain. 417-420. 10.1109/ICPC2T48082.2020.9071494.

V. G. V. Mahesh, C. Chen, V. Rajangam, A. N. J. Raj and P. T. Krishnan, "Shape and Texture Aware Facial Expression Recognition Using Spatial Pyramid Zernike Moments and Law’s Textures Feature Set," in IEEE Access, vol. 9, pp. 52509-52522, 2021, doi: 10.1109/ACCESS.2021.3069881.

S, N., N, P., & P, N. (2023). A Study on Flower Classification Using Deep Learning Techniques. International Journal of Computer Engineering in Research Trends, 10(4), 161–166. Doi:10.22362/ijcertpublications.v10i4.9

P. Tan, G. Tan and Z. Cai, "Dual-tree complex wavelet transform-based feature extraction for brain computer interface," 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Zhangjiajie, China, 2015, pp. 1136-1140, doi: 10.1109/FSKD.2015.7382102.

Jaiswal, S., & Pandey, M. K. P. (2023). Deep Artificial Neural Network based Blind Color Image Watermarking in YCbCr Color Domain using statistical features. International Journal of Computer Engineering in Research Trends, 10(3), 90–98. doi: 10.22362/ijcert.v10i3.21

Z. Liu, E. Blasch, Z. Xue, J. Zhao, R. Laganiere and W. Wu, "Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 1, pp. 94-109, Jan. 2012, doi: 10.1109/TPAMI.2011.109.

G. Piella and H. Heijmans, "A new quality metric for image fusion," Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), Barcelona, Spain, 2003, pp. III-173, doi: 10.1109/ICIP.2003.1247209.

Zhang, Chengfang. (2020). Multifocus image fusion using multiscale transform and convolutional sparse representation. International Journal of Wavelets, Multiresolution and Information Processing. 19. 2050061. 10.1142/S0219691320500617.

DTCWT flow diagram




How to Cite

K. . S., “An Effective Multi-focus Image Fusion Based on Law’s of Texture Enegery Measures in Integrated Wavelet Domain”, Int J Intell Syst Appl Eng, vol. 11, no. 6s, pp. 67–75, May 2023.



Research Article