目的 单一图像往往难以捕获一个场景下所有的细节信息,针对这一问题,可以通过多传感器或同一传感器的不同方式来获取多幅图像,然后通过图像融合技术将获得的多幅图像进行融合.为了提高图像融合的质量,提出一种基于快速离散Curvelet变换(FDCT)的图像融合新方法.方法 不同于以往的方法,提出一组新的融合规则.分别采用基于局部能量和改进拉普拉斯能量和的方法,通过对FDCT分解得到的低频和高频系数进行系数选择,然后对得到的融合系数进行FDCT逆变换重构得到融合图像.结果 通过对大量的多模态医学图像、红外可见光图像以及多聚焦图像进行图像融合实验,无论是运用视觉的主观评价,还是均值、标准差、信息熵以及边缘信息保持度等客观评价标准,本文方法都优于传统的基于像素平均、小波变换、FDCT以及双边梯度等融合方法.结论 对比现有的方法,本文方法对多模态和多聚焦等形式的图像融合都表现出优越的融合性能.

Objective A single-captured image of a real-world scene is frequently insufficient to reveal all details. To address this problem, images of the same scene captured by multiple sensors or by the same sensor but with different properties are typically combined into a single image by using image fusion techniques. A novel technique based on fast discrete curvelet transform (FDCT) for improving image fusion quality is presented in this study. Method Source images are initially decomposed via FDCT. A new fusion rule is subsequently proposed to fuse low-frequency and high-frequency coefficients; this rule is unlike those in previous image fusion methods. Low-frequency coefficients are fused by local energy, whereas high-frequency coefficients are fused by sum-modified-Laplacian. The most important feature information can be selected as the fused coefficients by applying the fusion rule. Finally, inverse FDCT is applied to reconstruct the resultant image using the fused coefficients. Result Several images, including multimodal medical, infrared-visible, and multifocus images, are used in the experiments. Experimental results demonstrate that the proposed technique is better than traditional methods, such as pixel averaging, wavelet transform, and other state-of-the-art methods,including FDCT and the method presented based on bilateral gradient, in terms of both subjective and objective evaluations. Conclusion The proposed fusion algorithm can obtain the most important feature information and exhibits superior performance to other methods in terms of multimodal and multifocus image fusion.