基于粒子群算法的图像融合扩展数码相机的景深

V. Aslantaş, Rifat Kurban
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引用次数: 3

摘要

由于景深有限的问题,严重影响了光学相机的成像效果。也就是说,位于相机焦点前后的物体是模糊的。将不同对焦设置所捕获的图像的聚焦区域进行组合,即可获得全焦图像。本文提出了一种基于区域的空间域图像融合方法,该方法基于从多聚焦源图像中选择更清晰的区域。采用粒子群算法对块型区域的大小进行优化。不同图像集的定量和主观实验结果表明,该方法优于传统的基于小波变换和拉普拉斯金字塔的方法以及基于遗传算法(GA)的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Extending depth-of-field of a digital camera using particle swarm optimization based image fusion
Images obtained by an optical camera are seriously affected from the limited depth of the field issue. That is, the objects located in-front-of or behind the focus of the camera are blurred. Everywhere-in-focus images can be obtained by combining focused regions of images which were captured by different focal settings. In this paper, an optimal region based spatial domain image fusion approach based on selecting sharper regions from the multi-focus source images is proposed. Size of the block type regions are optimized by using particle swarm optimization (PSO) algorithm. Quantitative and subjective experimental results of different image sets show that proposed method is better than traditional wavelet and Laplacian pyramid based and also genetic algorithm (GA) based methods.
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