An optimization-based approach to image fusion using structural similarity
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This thesis examines a framework for solving optimization problems involving structural similarity index measure Mean. Image fusion involves combining multiple images to preserve their most desirable characteristics. The objective is to create a single enhanced image that can be utilized for various applications, including human visual perception, object detection, and target recognition. A general framework is introduced for the formulation of image fusion, which incorporates the SSIM, one of the most effective measures of visual proximity that is consistent with the human visual system. The concept of image fusion is revisited, its importance is emphasized, and an overview of methods commonly used for image fusion is provided. An alternative expression of SSIM using Mean values and vector norms is proposed, along with the calculation of its derivative. Next, the SSIM Mean is used to formulate image fusion as data fidelity of an optimization problem. Gradient-based methods are applied to solve it. A comparison is conducted between the proposed method and the method presented by Brunet in his Ph.D. thesis at UWaterloo. The experiment use test images in MATLAB. The evaluation metrics MSE, PSNR, and SSIM show expected results in assessing the experimental outcomes. The significant advantage of the proposed formulation is its flexibility in accommodating different degradation operators and regularization terms.