Hybrid Multi-Focus Image Fusion using Stationary Wavelet Transform and Focus Measures for Visual Sensor Networks
Nainavarapu Radha1, Tummala Ranga Babu2
1Nainavarapu Radha*, Research Scholar, Department of Electronics and Communications Engineering, University College of Engineering and Technology, Acharya Nagarjuna University, Guntur, India.
2Tummala Ranga Babu, Department of Electronics and Communications Engineering, RVR & JC College of Engineering, Guntur, India.

Manuscript received on November 10, 2019. | Revised Manuscript received on November 17, 2019. | Manuscript published on 30 November, 2019. | PP: 3765-3769 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8141118419/2019©BEIESP | DOI: 10.35940/ijrte.D8141.118419

Open Access | Ethics and Policies | Cite  | Mendeley | Indexing and Abstracting
© 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: The Multifocal 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 discrete wavelet-based fusion algorithms lead artifacts into the fused image due to its shift variance. But shift invariance is essential in image fusion during the reconstruction of the fused image without any loss. The Stationary Wavelet Transform is one of the most precious ones, eliminating shift variance caused by the discrete wavelet transform. And also focus measures are essential for the selection of focused objects in multi-focused images in order to get a fused image with every object in focus. Thus the advantages of Stationary wavelet transform and focus measures are considered for fusion in this paper. The proposed fusion method not only produces a focused fused image without artifacts and its performance is also good compared to other fusion methods.
Keywords: Stationary Wavelet Transform, sum modified Laplacian, Spatial Frequency, Focus Measures.
Scope of the Article: Sensor Networks, Actuators for Internet of Things.