基于二代Curvelet变换耦合图像纹理算法

当前较多图像融合算法主要是通过图像的能量信息来完成系数融合，忽略了图像的纹理特征，导致融合结果中存在吉布斯以及块现象等缺陷。设计了二代Curvelet变换耦合纹理信息调节的融合算法，该算法采用二代Curvelet变换，从输入图像中获取不同的Curvelet系数，采用图像的R(Red),G(Green),B(Blue)值，构造纹理信息因子，测量图像的纹理信息，并联合图像的信息熵特征，定义低频信息融合机制，完成低频系数的融合，使融合图像具有更多的纹理信息。利用图像的平均梯度特征建立高频信息融合方法，实现高频系数的融合，使其含有更多的边缘细节信息。测试结果显示：与已有的融合算法相比，该算法的融合图像更为清晰，没有吉布斯和块现象，具有更大的交互信息量和标准差值。

Multi-focus image fusion algorithm based on second generation Curvelet transform coupled with texture information adjustment

At present, many image fusion algorithms mainly use image energy information to fuse image coefficients, ignoring the texture information of the image, which brings the defects of Gibbs and block phenomenon to the fusion results. This paper designs a multi-focus image fusion algorithm based on the second generation Curvelet transform coupled with texture information adjustment. Firstly, the second generation Curvelet transform is utilized to obtain different sub-band images from the input image. Then, the texture information factor is constructed by using R,G and B values of the image, and the texture information of the image is measured. By combining the information entropy characteristics of the image and the R, G and B values of the image, the fusion results have more texture information. The average gradient feature of the image is adopted to compute high frequency coefficient fusion, which makes it more capable of describing details such as edges. Finally, the image fusion test of this algorithm shows that compared with current fusion algorithms, the fusion image of this proposed algorithm is clearer, without the defects of Gibbs and block phenomenon, and with larger values of mutual information and standard deviation.