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Fusion of multifocus images by lattice structures

机译：通过晶格结构融合多焦点图像

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Image fusion methods based on multiscale transform (MST) suffer from high computational load due to the use of fast Fourier transforms (ffts) in the lowpass and highpass filtering steps. Lifting wavelet scheme which is based on second generation wavelets has been proposed as a solution to this issue. Lifting Wavelet Transform (LWT) is composed of split, prediction and update operations all implemented in the spatial domain using multiplications and additions, thus computation time is highly reduced. Since image fusion performance benefits from undecimated transform, it has later been extended to Stationary Lifting Wavelet Transform (SLWT). In this paper, we propose to use the lattice filter for the MST analysis step. Lattice filter is composed of analysis and synthesis parts where simultaneous lowpass and highpass operations are performed in spatial domain with the help of additions/multiplications and delay operations, in a recursive structure which increases robustness to noise. Since the original filter is designed for the undecimated case, we have developed undecimated lattice structures, and applied them to the fusion of multifocus images. Fusion results and evaluation metrics show that the proposed method has better performance especially with noisy images while having similar computational load with LSWT based fusion method. (C) 2016 Elsevier Inc. All rights reserved.
机译：由于在低通和高通滤波步骤中使用了快速傅立叶变换（ffts），因此基于多尺度变换（MST）的图像融合方法会承受较高的计算量。已经提出了基于第二代小波的提升小波方案作为该问题的解决方案。提升小波变换（LWT）由拆分，预测和更新操作组成，所有这些操作都在空间域中使用乘法和加法实现，因此大大减少了计算时间。由于图像融合性能得益于未抽取的变换，因此后来已扩展到固定提升小波变换（SLWT）。在本文中，我们建议在MST分析步骤中使用晶格滤波器。格形滤波器由分析和合成部分组成，其中递归结构以增加抗噪声能力的递归结构在空间域中同时执行低通和高通运算。由于原始滤波器是针对未抽取的情况设计的，因此我们开发了未抽取的晶格结构，并将其应用于多焦点图像的融合。融合结果和评估指标表明，该方法具有更好的性能，尤其是在噪点较大的图像上，同时与基于LSWT的融合方法具有相似的计算负荷。 （C）2016 Elsevier Inc.保留所有权利。

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