A Comprehensive Survey of Multimodal Image Fusion Schemes
Bhavna Bharath1, Suganthi N2
1Bhavna Bharath, Assistant Professor, Department of Computer Science & Engineering, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
2Dr. Suganthi N, Assistant Professor, Department of Computer Science & Engineering, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
Manuscript received on 12 December 2018 | Revised Manuscript received on 23 December 2018 | Manuscript Published on 09 January 2019 | PP: 241-246 | Volume-7 Issue-4S November 2018 | Retrieval Number: E1916017519/19©BEIESP
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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: Multimodal images are scenes with anatomy details that are captured using two different devices. Different imaging techniques give complementary details about what is visualized. Infrared and visual images are examples of multimodal images that are fused together in order to obtain a single comprehensive fused image. Combining multimodal images yield enhanced features for image analysis, feature extraction and detection. Infrared and Visual image fusion will fuse the source images into single extensive image to raise image quality .This will in turn decrease the redundancy in image data. This is broadly used in different applications to improve the perception of the scene. The reliability, accuracy and complementary details of the scene in the resultant fused image makes these approaches be used in multiple areas. Recently, many fusion methods have been formulated due to the sprouting demands & advancement of image depiction schemes. However, a unified survey paper about this field has not been published in a few years. Consequently, we make a survey report to record the methodical advancements of visual and infrared image fusion. In this paper, firstly the overview of applications of IR and VI image fusion is represented. Secondly, we present the existing state of the art fusion techniques. Finally, image quality metrics are discussed to measure the efficiency of the fusion algorithm. Although, this survey halts with various fusion methods that have been proposed earlier there is still room for improvement in research in the field of multimodal image fusion.
Keywords: Multimodal, Image Analysis, Image Fusion.
Scope of the Article: Image Security