激光与光电子学进展, 2018, 55 (7): 071015, 网络出版: 2018-07-20
一种基于深度学习的多聚焦图像融合算法 下载: 856次
A Multi-Focus Image Fusion Algorithm Based on Depth Learning
图像处理 多聚焦图像融合 深度学习网络 矫正矩阵 图像块分类 边界修复 image processing multi-focus image fusion deep learning network correction matrix image block classification boundary restoration
摘要
针对深度学习在计算机视觉上的良好表现,提出一种基于深度学习的多聚焦图像融合算法,在原有的AlexNet网络模型基础上改进了卷积核大小、步长等;利用改进后的深度学习网络特有的得分机制分类了聚焦图像块与散焦图像块;使用矫正矩阵矫正了误判图像块,并细分、修复了融合后的图像聚焦与散焦分界区域,得到了融合图像;选取6组多聚焦图像验证了本文算法的有效性。实验结果表明:与其他算法相比, 运用本文算法进行图像融合,能够保存较多的图像原始高频信息,并在互信息、边缘信息保持度、平均梯度和熵等评价指标上取得了较好的表现。
Abstract
Aiming at the good performance in computer vision for depth learning, a multi-focus image fusion algorithm based on depth learning is proposed. Based on the existing AlexNet network model, the convolution kernel size and step size are improved. The focused image blocks and the defocused image block are classified by using the improved scoring mechanism of deep learning network. Then, the correction matrix is used to correct the misjudgment image blocks. The boundary zone of image focus and defocus is subdivided and repaired. Six pairs of multi-focus images are randomly selected to verify the effectiveness of the proposed method. The experimental results show that, compared with other algorithms, the fusion results obtained by this algorithm can preserve the original high-frequency information of the image, and achieve good performance on four evaluation indexes of mutual information, edge information retention, entropy and average gradient.
陈清江, 李毅, 柴昱洲. 一种基于深度学习的多聚焦图像融合算法[J]. 激光与光电子学进展, 2018, 55(7): 071015. Chen Qingjiang, Li Yi, Chai Yuzhou. A Multi-Focus Image Fusion Algorithm Based on Depth Learning[J]. Laser & Optoelectronics Progress, 2018, 55(7): 071015.