Abstract
Image fusion is one of the most rapidly evolving fields in image processing today, and its applications are widely expanded in various fields. In the field of image fusion, the method based on multi-scale decomposition plays an important role. However, it faces many difficult puzzles, such as the risk of over-smoothing during decomposition, blurring of fusion results, and loss of details. Aiming at these problems, this paper proposes a novel decomposition-based image fusion framework, which overcomes the problems of noise, blurring, and loss of details. Both the symmetry and asymmetry between infrared and visible images are important research hotspots in this paper. The experiments confirmed that the fusion framework outperforms other methods in both subjective observation and objective evaluation.
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