shearlet变换和区域特性相结合的图像融合

摘要

为了提高多模医学图像或多聚焦图像的融合性能，结合shearlet变换能够捕捉图像细节信息的性质，提出了一种基于shearlet变换的图像融合算法。首先，用shearlet变换将已精确配准的两幅原始图像分解，得到低频子带系数和不同尺度不同方向的高频子带系数。低频子带系数使用改进的加权融合算法，用平均梯度来计算加权参量，以此来改善融合图像轮廓模糊度高的问题，高频子带系数采用区域方差和区域能量相结合的融合规则，以得到丰富的细节信息。最后，进行shearlet逆变换得到融合图像。结果表明，此算法在主观视觉效果和客观评价指标上优于其它融合算法。%In order to improve the performance of multi-modality medical image fusion and multi-focus image fusion, since the shearlet transform can capture the detail information of images, an image fusion algorithm based on shearlet transform was proposed.Firstly, the shearlet transform was used to decompose the two registered original images, thus the low frequency sub-band coefficients and high frequency sub-band coefficients of different scales and directions were obtained.The fusion principle of low frequency sub-band coefficients was based on the method of weighted fusion, using the average gradient to calculate the weighted parameters in order to improve the edge fuzzy of the fused image.As for the high frequency sub-band coefficients, a fusion rule adopting the region variance combining with the region energy to get the detail information was presented.Finally, the fused image was reconstructed by inverse shearlet transform.The results show that the algorithm is superior to other fusion algorithms on subjective visual effect and objective evaluation.