Image Fusion using Non Subsampled Contourlet Transform in Medical Field
Jampani Ravi1, M. Gowri Sri Durga2, Y. D. R. Ch. Kartheek3, MD. Shabeena Begum4, T. Raju5, T. V. Syamala Raju6
1Jampani Ravi*, Assistant Professor, Department of ECE, SRKR Engineering College, Bhimavaram, India.
2M. Gowri Sri Durga, Student, Department of ECE, SRKR Engineering College, Bhimavaram, India.
3Y. D. R. Ch. Kartheek, Student, Department of ECE, SRKR Engineering College, Bhimavaram, India.
4MD. Shabeena Begum, Student, Department of ECE, SRKR Engineering College, Bhimavaram, India.
5T. Raju, Department of ECE, SRKR Engineering College, Bhimavaram, India.
6T. V. Syamala Raju, Assistant Professor, Department of ECE, SRKR Engineering College, Bhimavaram, India.
Manuscript received on January 24, 2020. | Revised Manuscript received on February 15, 2020. | Manuscript published on February 29, 2020. | PP: 3829-3832 | Volume-9 Issue-3, February 2020. | Retrieval Number: C6268029320/2020©BEIESP | DOI: 10.35940/ijeat.C6268.029320
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Image fusion is a powerful method and developing field in the area of image processing. The image fusion is a type of methodology that combines the two or more images into single more informative image. Image fusion is the process of assimilation of numerous input images into a new single fused image with highly informative than the input image. There are various image fusion transform techniques are proposed. Out of that techniques a Non-subsampled Counterlet transform includes shift invariant property, highly directionality, reduced the cost and more efficient information as compared to previous techniques such as wavelet transform(WT), DWT, LWT, MWT, CWT, Curvelet transform, Contourlet transform. In NSCT, we decompose the images into low frequency and high frequency using sparse representation and absolute-maximum rule respectively. The DGSR algorithm is used for the better performance of SR-based approach. Finally, to reconstruct the image we use inverse NSCT and output is fused image.
Keywords: Image fusion, NSCT, SPARSE, SENSOR.