Multifocus image fusion scheme based on features of multiscale products and PCNN in lifting stationary wavelet domain
Multifocus image fusion scheme based on features of multiscale products and PCNN in lifting stationary wavelet domain
复制标题
提升平稳小波域基于多尺度乘积特征和PCNN的多聚焦图像融合方案
DOI：
10.1016/j.optcom.2010.10.056
复制
发表时间：
2011-03-01
影响因子：
2.4
通讯作者：
Guo, M. Y.
中科院分区：
文献类型：
--
作者：
Chai, Y.;Li, H. F.;Guo, M. Y.
多聚焦图像融合的目的是通过将同一场景的多幅图像信息融合，克服成像相机的有限景深。针对同一场景多聚焦图像的融合问题，提出了一种基于提升平稳小波变换（LSWT）和改进的脉冲耦合神经网络（PCNN）的多尺度积的融合算法，其中每个神经元的连接强度可以自适应地选择。为了在噪声环境下与多聚焦图像融合后系数的合理选取，分别讨论了低频子带系数和带通子带系数的选取原则。在低频子带系数的选取上，采用一种新的低频子带和改进拉普拉斯算子（NSML）作为输入，激励PCNN神经元，该算子能有效地表示图像在LSWT域的显著特征和清晰边界;当选择高频子带系数时，提出了一种新的多尺度乘积的拉普拉斯算子的局部邻域和，并将其作为高频特征之一来激励PCNN神经元。选取激发时间较大的LSWT域系数作为融合图像的系数。实验结果表明，在噪声环境下，该融合方法在视觉质量和客观评价方面均优于传统的基于离散小波变换（DWT）、基于LSWT和基于LSWT-PCNN的图像融合方法. (C)2010 Elsevier B. V.保留所有权利。
Multifocus image fusion aims at overcoming imaging cameras's finite depth of field by combining information from multiple images with the same scene. For the fusion problem of the multifocus image of the same scene, a novel algorithm is proposed based on multiscale products of the lifting stationary wavelet transform (LSWT) and the improved pulse coupled neural network (PCNN), where the linking strength of each neuron can be chosen adaptively. In order to select the coefficients of the fused image properly with the source multifocus images in a noisy environment, the selection principles of the low frequency subband coefficients and bandpass subband coefficients are discussed, respectively. For choosing the low frequency subband coefficients, a new sum modified-Laplacian (NSML) of the low frequency subband, which can effectively represent the salient features and sharp boundaries of the image in the LSWT domain, is an input to motivate the PCNN neurons; when choosing the high frequency subband coefficients, a novel local neighborhood sum of Laplacian of multiscale products is developed and taken as one type of feature of high frequency to motivate the PCNN neurons. The coefficients in the LSWT domain with large firing times are selected as coefficients of the fused image. Experimental results demonstrate that the proposed fusion approach outperforms the traditional discrete wavelet transform (DWT)-based, LSWT-based and LSWT-PCNN-based image fusion methods even though the source image is in a noisy environment in terms of both visual quality and objective evaluation. (C) 2010 Elsevier B.V. All rights reserved.