FS-Transformer: A new frequency Swin Transformer for multi-focus image fusion

• Weiping Jiang,
• Yan Wei,
• Hao Zhai,

Vol. 18, No. 7, July 31, 2024
10.3837/tiis.2024.07.010, Download Paper (Free):

• Multi-focus image fusion
• Swin Transformer
• wavelet transform
• Deep Learning

Abstract

In recent years, multi-focus image fusion has emerged as a prominent area of research, with transformers gaining recognition in the field of image processing. Current approaches encounter challenges such as boundary artifacts, loss of detailed information, and inaccurate localization of focused regions, leading to suboptimal fusion outcomes necessitating subsequent post-processing interventions. To address these issues, this paper introduces a novel multi-focus image fusion technique leveraging the Swin Transformer architecture. This method integrates a frequency layer utilizing Wavelet Transform, enhancing performance in comparison to conventional Swin Transformer configurations. Additionally, to mitigate the deficiency of local detail information within the attention mechanism, Convolutional Neural Networks (CNN) are incorporated to enhance region recognition accuracy. Comparative evaluations of various fusion methods across three datasets were conducted in the paper. The experimental findings demonstrate that the proposed model outperformed existing techniques, yielding superior quality in the resultant fused images.

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