Multi-Focus Image Fusion Based on Fractal Dimension and Parameter Adaptive Unit-Linking Dual-Channel PCNN in Curvelet Transform Domain

作者：Liangliang Li, Sensen Song, Lv Ming, Zhenhong Jia, Hongbing Ma · 发表于：Fractal and Fractional · 年份：2025 · DOI：10.3390/fractalfract9030157 · 被引用次数：17 · 研究领域：Advanced Image Fusion Techniques、Image and Signal Denoising Methods、Photoacoustic and Ultrasonic Imaging

Multi-focus image fusion is an important method for obtaining fully focused information. In this paper, a novel multi-focus image fusion method based on fractal dimension (FD) and parameter adaptive unit-linking dual-channel pulse-coupled neural network (PAUDPCNN) in the curvelet transform (CVT) domain is proposed. The source images are decomposed into low-frequency and high-frequency sub-bands by CVT, respectively. The FD and PAUDPCNN models, along with consistency verification, are employed to fuse the high-frequency sub-bands, the average method is used to fuse the low-frequency sub-band, and the final fused image is generated by inverse CVT. The experimental results demonstrate that the proposed method shows superior performance in multi-focus image fusion on Lytro, MFFW, and MFI-WHU datasets.