A Low Overhead Image Registration Algorithm using DWT and WIPSO for Resource Constrained SBC based Embedded System Application
Sameer Kumar Das1, Jitendra Pramanik2, Abhaya Kumar Samal3, Nibedita Adhikari4

1Sameer Kumar Das, Research Scholar, Biju Patnaik University of Technology , Odisha , India.
2Jitendra Pramanik, Assistant Professor, Centurion University of Technology and Management, Odisha, India.
3Dr. Abhaya Kumar Samal Professor, Trident Academy of Technology, Odisha , India.

4Dr. Nibedita Adhikari , Biju Patnaik University of Technology, Odisha., India.

Manuscript received on 03 August 2019. | Revised Manuscript received on 09 August 2019. | Manuscript published on 30 September 2019. | PP: 6190-6199 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5543098319/2019©BEIESP | DOI: 10.35940/ijrte.C5543.098319
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© 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 registration is a vital but integral component of any image processing application. Fundamentally, image registration involves transforming two or more sets of image data derived from different sources, collected at different instant of times, taken from different perspectives and from different viewpoints, all from different frames of reference into an image in a single coordinate system, a single frame of reference. Over the years, there have been many image registration techniques developed and used in variety of application fields. This paper proposes a computationally low overhead image registration technique that has been tested with variety of images using weight improved particle swarm optimization (WIPSO) algorithm integrated with discrete wavelet transform (DWT). Fundamentally, the proposed scheme comprises of a two-step approach, where the first step involves extraction of the random image from the source image which will serve as an ingredient for the formation of the particle swarm to be used in the WIPSO and the second step involves an explorative search for the target image in the area of interest from the selected population using WIPSO technique. Extensive simulation has established the effectiveness of the proposed soft-computing technique for variety of image registration application; even suitable for deployment in many resource-constrained single board computer based embedded system applications.
Keywords: Particle Swarm Optimization (PSO), Weight Improved PSO, Digital Image Processing, Discrete Wavelet Transform, Weight Improved Particle Swarm Optimization (WIPSO)

Scope of the Article:
Signal and Image Processing