基于Shearlet变换的多聚焦图像融合方法

为提高多聚焦图像的融合效果,利用Shearlet变换具有多尺度多方向的特性,文中提出了一种基于Shearlet变换的图像融合算法。针对待融合图像进行Shearlet变换,得到低频子带系数和不同尺度不同方向的高频子带系数;对低频子带系数取分解系数区域能量高的系数,高频子带系数采用区域能量和区域清晰度以及区域方差相结合,采用多判别法得到融合系数,并最终进行Shearlet逆变换得到融合图像。结果表明,在主观视觉效果和客观评价指标上此算法优于其他融合算法

Multi-focus Image Fusion Method Research Based on Shearlet Transform

This paper proposes an image fusion algorithm based on the multi-scale multi-direction Shearlet transform to improve the performance of multi-focus image fusion.First,the Shearlet transform was used to decompose the registered original images,thus the low frequency sub-band coefficients and high frequency sub-band coefficients of different scales and directions were obtained.The coefficients of high regional energy were taken as the low frequency sub-band coefficients to yield the high frequency sub-band coefficient.The fusion coefficients are determined by the multiple discriminant method combining the regional energy and regional definition as well as regional variance.Finally,the fusion image is obtained by the inverse Shearlet transform.The results show that the subjective visual performance and objective evaluation index of this algorithm are superior to other fusion algorithms.