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Faculty of Graduate Studies

Sanduni Premaratne

  • MPhil (University of Moratuwa, 2019)

  • BSc (University of Moratuwa, 2016)

Notice of the Final Oral Examination for the Degree of Master of Applied Science

Topic

Infrared-Visible Image Fusion in the Gradient Domain

Department of Electrical and Computer Engineering

Date & location

  • Friday, March 1, 2024

  • 12:30 A.M.

  • Virtual Defence

Reviewers

Supervisory Committee

  • Dr. Panajotis Agathoklis, Department of Electrical and Computer Engineering, University of Victoria (Co-Supervisor)

  • Dr. Leonard Bruton, Department of Electrical and Computer Engineering, UVic (Co-Supervisor)

  • Dr. Daniela Constantinescu, Department of Mechanical Engineering, UVic (Non-unit Member) 

External Examiner

  • Dr. Daler Rakhmatov, Department of Electrical and Computer Engineering, University of Victoria 

Chair of Oral Examination

  • Dr. Scott McIndoe, Department of Chemistry, UVic

     

Abstract

Due to the complementary properties of the infrared cameras compared to conventional visible imaging cameras, it has become increasingly popular to fuse infrared and visible images of the same scene for better visual understanding. One major application of this is surveillance which involves videos and requires fast processing. Therefore, there is a need for investigating novel low-complexity fusion algorithms that can be implemented in real-time applications.

In this study, we address this critical research problem by two-scale fusion in the gradient domain with saliency detection and image enhancement. In the proposed method, the source images are first decomposed in to base and detail layers. Next, the base parts are fused in the gradient domain by choosing the maximum absolute gradient, whereas the gradients of the detail parts are fused using a weighted average where the weights are calculated using saliency maps. Prior to fusion, the detail parts are enhanced using a guided filter-based enhancement approach. Finally, the fused gradients of the base and detail components are added together to obtain the gradients of the fused image, from which the fused image is reconstructed using a re construction technique based on wavelets. Experimental results demonstrate that the proposed method achieves very competitive performance in subjective and objective fusion assessments, while also outperforming most methods in terms of computational complexity.

 

Index terms- Image fusion, IR images, visible images, computational complexity, gradient domain

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