PATN: Polarized Attention based Transformer Network for Multi-focus image fusion

• Pan Wu,
• Zhen Hua,
• Jinjiang Li,

Vol. 17, No. 4, April 30, 2023
10.3837/tiis.2023.04.011, Download Paper (Free):

• Image fusion
• Multi-focus
• Polarized Attention
• Transformer

Abstract

In this paper, we propose a framework for multi-focus image fusion called PATN. In our approach, by aggregating deep features extracted based on the U-type Transformer mechanism and shallow features extracted using the PSA module, we make PATN feed both long-range image texture information and focus on local detail information of the image. Meanwhile, the edge-preserving information value of the fused image is enhanced using a dense residual block containing the Sobel gradient operator, and three loss functions are introduced to retain more source image texture information. PATN is compared with 17 more advanced MFIF methods on three datasets to verify the effectiveness and robustness of PATN.

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