Multi-focus image fusion based on pulse coupled neural network and WSEML in DTCWT domain DOI Creative Commons
Yuan Jia, Teng Ma

Frontiers in Physics, Год журнала: 2025, Номер 13

Опубликована: Апрель 2, 2025

The goal of multi-focus image fusion is to merge near-focus and far-focus images the same scene obtain an all-focus that accurately comprehensively represents focus information entire scene. current algorithms lead issues such as loss details edges, well local blurring in resulting images. To solve these problems, a novel method based on pulse coupled neural network (PCNN) weighted sum eight-neighborhood-based modified Laplacian (WSEML) dual-tree complex wavelet transform (DTCWT) domain proposed this paper. source are decomposed by DTCWT into low- high-frequency components, respectively; then average gradient (AG) motivate PCNN-based rule used process low-frequency WSEML-based components; we conducted simulation experiments public Lytro dataset, demonstrating superiority algorithm proposed.

Язык: Английский

Multi-Focus Image Fusion Based on Fractal Dimension and Parameter Adaptive Unit-Linking Dual-Channel PCNN in Curvelet Transform Domain DOI Creative Commons
Liangliang Li, Sensen Song,

Ming Lv

и другие.

Fractal and Fractional, Год журнала: 2025, Номер 9(3), С. 157 - 157

Опубликована: Март 3, 2025

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

Язык: Английский

Процитировано

0
Multi-focus image fusion based on pulse coupled neural network and WSEML in DTCWT domain DOI Creative Commons
Yuan Jia, Teng Ma

Frontiers in Physics, Год журнала: 2025, Номер 13

Опубликована: Апрель 2, 2025

The goal of multi-focus image fusion is to merge near-focus and far-focus images the same scene obtain an all-focus that accurately comprehensively represents focus information entire scene. current algorithms lead issues such as loss details edges, well local blurring in resulting images. To solve these problems, a novel method based on pulse coupled neural network (PCNN) weighted sum eight-neighborhood-based modified Laplacian (WSEML) dual-tree complex wavelet transform (DTCWT) domain proposed this paper. source are decomposed by DTCWT into low- high-frequency components, respectively; then average gradient (AG) motivate PCNN-based rule used process low-frequency WSEML-based components; we conducted simulation experiments public Lytro dataset, demonstrating superiority algorithm proposed.

Язык: Английский

Процитировано

0