基于区域特性的Curvelet变换图像融合算法

Image fusion algorithm with Curvelet transform based on regional feature

为克服小波变换在二维或更高维度空间分析中的缺陷,提高图像融合质量,提出基于二代Curvelet变换的图像融合改进算法。引入可以有效分析图像中的曲线奇异性,能更加合理处理图像边缘信息的Curvelet变换对图像进行分解,对图像分解后的低频部分采用自适应阈值的区域方差高斯加权融合方法,增加图像像素之间的关联,并有效保留细节和边缘。对高频部分采用区域能量融合方法来降低噪声,增强图像的细节。采用该算法对多组不同图像进行融合实验,并用信息熵、交叉熵、相关系数、空间频率等对融合图像进行客观评价。实验结果表明,该算法优于传统的融合规则和算法,能在保持更好清晰度的同时获得更丰富的图像内容。

In order to overcome the weakness of wavelet transform in 2D or higher dimensional spatial analysis and improve quality of image fusion,a modified image fusion algorithm based on Curvelet transform is proposed. Curvelet transform which can effectively analyze singularity of a curve and rationally process edge information in an image is introduced to decompose images. An adaptive threshold regional variance and Gaussian-weighted fusion algorithm is used in low-frequency region to en-hance the correlation between pixels in an image and preserve its details and edges effectively. The regional energy fusion method is applied in high-frequency region to reduce noise and enhance the details of the image. Many fusion experiments of different images were carried out with the algorithm. The fusion results were evaluated by information entropy,cross entropy,correlation coefficient and space frequency. The experiment results indicate that the proposed algorithm is more outstanding than the conven-tional fusion rules and methods,and can obtain better resolution and more rich image content.