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Image Fusion With Cosparse Analysis Operator
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DOI：10.1109/LSP.2017.2696055.png)
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Abstract
En 中文
The letter addresses the image fusion problem, where multiple images captured with different focus distances are to be combined into a higher quality all-in-focus image. Most current approaches for image fusion strongly rely on the unrealistic noise-free assumption used during the image acquisition, and then yield limited fusion robustness. In our approach, we formulate the multifocus image fusion problem in terms of an analysis sparse model, and simultaneously perform the restoration and fusion of multifocus images. Based on this model, we propose an analysis operator learning, and define a novel fusion function to generate an all-infocus image. Experimental evaluations confirm the effectiveness of the proposed fusion approach both visually and quantitatively, and show that our approach outperforms the state-of-the-art fusion methods.
Keywords:
Alternating direction method of multiplier (ADMM)
analysis K-singular value decomposition (K-SVD)
analysis operator learning
cosparse representation
multifocus image fusion
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