#4284. Multi-focus image fusion based on multi-scale sparse representation

September 2026publication date
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Journal’s subject area:
Visual Arts and Performing Arts;
Communication;
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Abstract:
Although colorful information in natural scenes can be collected, due to the limitation of camera depth of field, it is hard to capture an image with all-in-focus. Sparse representation (SR)-based methods have shown their powerful potentiality and ability in multi-focus image fusion. However, because of sparse coding and information compress, the existing fusion methods based on SR are imperfect to seize the rich details and significant texture information in source images. Source images are processed by multi-scale decomposition and sub-images with different scales can be obtained. According to image features with different richness in these sub-images, dictionaries with different sizes and redundancy are thereby trained. Finally, a fused image with all-in-focus can be obtained by sparse reconstruction and inverse multi-scale decomposition. The proposed MIF-MsSR not only reserves the integrity of the information in source images, but also has better fusion performance on subjective and objective indicators than other state-of-the-art methods.
Keywords:
Adaptive fusion rule; Multi-focus image fusion; Multi-scale decomposition; Sparse representation; Sum modified Laplacian

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