The main aim of image fusion is to integrate the qualitative visual information from multiple images into a single image. Image fusion is implemented in spatial and transform domains. The implementation of algorithm in spatial domain is simple. But, the images are stored/transmitted using popular methods like JPEG and JPEG2000, which are implemented in the transform domain. Therefore fusion algorithms in spatial domain are not suitable for real time application. Image transforms are categorized as block-based and multi resolution-based transforms. In this study, block-based transforms such as Hadamard Transform (HT), Discrete Cosine Transform (DCT), Haar Transform (HrT), and Slant Transform (ST) are considered for image fusion. The DCT based approaches are suffering from undesirable side effects such as blurring and blocking artifacts that reduce the quality of the fused image. In this paper, the Human Visual System (HVS) model is considered to select the appropriate block from multiple images to obtain the fused image. The proposed approach is applied to all the block-based transforms to assess the performance. Methods such as Mutual Information (MI), Edge Strength and Orientation Preservation (ESOP), Feature Similarity Index (FSIM), Normalized Cross Correlation (NCC) and Score are used to assess the performance of the proposed algorithms. The experimental results indicate that the proposed method is better in terms of improved quality and reduced blocking artifacts.
Journal of Signal Processing Systems – Springer Journals
Published: Jul 5, 2017
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