基于梯度正则化卷积稀疏表示的多聚焦噪声图像融合

Xuanjing Shen, Yunqi Zhang, Haipeng Chen, Di Gai
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引用次数: 0

摘要

该方法提出了一种结合梯度正则化卷积稀疏表示和空间频率的多焦点噪声图像融合算法。首先,通过二尺度图像分解将源图像分解为基层和细节层;细节层采用交替方向乘法器(ADMM)求解带有梯度惩罚的卷积稀疏系数,完成细节层系数的融合。然后,基层利用空间频率判断焦点区域,利用空间频率和“选择-最大”策略实现基层的多焦点融合结果。最后,将融合后的图像作为基础层和细节层的叠加进行计算。实验结果表明,与其他算法相比,该算法提供了良好的主观视觉感知和客观评价指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-focus noisy image fusion based on gradient regularized convolutional sparse representatione
The method proposes a multi-focus noisy image fusion algorithm combining gradient regularized convolutional sparse representatione and spatial frequency. Firstly, the source image is decomposed into a base layer and a detail layer through two-scale image decomposition. The detail layer uses the Alternating Direction Method of Multipliers (ADMM) to solve the convolutional sparse coefficients with gradient penalties to complete the fusion of detail layer coefficients. Then, The base layer uses the spatial frequency to judge the focus area, the spatial frequency and the "choose-max" strategy are applied to achieved the multi-focus fusion result of base layer. Finally, the fused image is calculated as a superposition of the base layer and the detail layer. Experimental results show that compared with other algorithms, this algorithm provides excellent subjective visual perception and objective evaluation metrics.
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