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A lightweight scheme for multi-focus image fusion

机译：一种轻量级的多焦点图像融合方案

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摘要

The aim of multi-focus image fusion is to fuse the images taken from the same scene with different focuses so that we can obtain a resultant image with all objects in focus. However, the most existing techniques in many cases cannot gain good fusion performance and acceptable complexity simultaneously. In order to improve image fusion efficiency and performance, we propose a lightweight multi-focus image fusion scheme based on Laplacian pyramid transform (LPT) and adaptive pulse coupled neural networks-local spatial frequency (PCNN-LSF), and it only needs to deal with fewer sub-images than common methods. The proposed scheme employs LPT to decompose a source image into the corresponding constituent sub-images. Spatial frequency (SF) is calculated to adjust the linking strength, beta of PCNN according to the gradient features of the sub-images. Then oscillation frequency graph (OFG) of the sub-images is generated by PCNN model. Local spatial frequency (LSF) of the OFG is calculated as the key step to fuse the sub-images. Incorporating LSF of the OFG into the fusion scheme (LSF of the OFG represents the information of its regional features); it can effectively describe the detailed information of the sub-images. LSF can enhance the features of OFG and makes it easy to extract high quality coefficient of the sub-image. The experiments indicate that the proposed scheme achieves good fusion effect and is more efficient than other commonly used image fusion algorithms.
机译：多焦点图像融合的目的是将从同一场景拍摄的图像与不同焦点融合在一起，以便获得所有对象都聚焦的合成图像。但是，在许多情况下，大多数现有技术无法同时获得良好的融合性能和可接受的复杂性。为了提高图像融合效率和性能，我们提出了一种基于拉普拉斯金字塔变换（LPT）和自适应脉冲耦合神经网络-局部空间频率（PCNN-LSF）的轻型多焦点图像融合方案，仅需处理与普通方法相比，子图像更少。所提出的方案利用LPT将源图像分解成相应的组成子图像。根据子图像的梯度特征，计算空间频率（SF）来调整PCNN的链接强度beta。然后通过PCNN模型生成子图像的振荡频率图（OFG）。 OFG的局部空间频率（LSF）被计算为融合子图像的关键步骤。将OFG的LSF合并到融合方案中（OFG的LSF表示其区域特征的信息）；它可以有效地描述子图像的详细信息。 LSF可以增强OFG的功能，并易于提取子图像的高质量系数。实验表明，该方案取得了较好的融合效果，并且比其他常用的图​​像融合算法更有效。

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