Lin Su-Zhen, Yang Feng-Bao, Chen Lei
Guang Pu Xue Yu Guang Pu Fen Xi. 2014 Apr;34(4):1144-50.
Fusion method of dual color mid-wave infrared images is presented in this paper in order to solve such frequently rising issues as limited contrast ratio improvement and serious marginal area distortion in the fusion of the above two images using multi-scale top-hat decomposition. The detailed procedure is shown as the following: A low-frequency component image and a sequence of support value images of the two subdivision band images of mid-wave infrared are obtained respectively with support value transform. Multi-scale bright and dim information are first extracted from the last layer of low-frequency image using the multi-scale top-hat decomposition method respectively. Then they are fused by selecting the maximum gray of each pixel in two subdivision band images of mid-wave infrared respectively. Following that, the two resulted images are enhanced using the gray-scale normalization and Gaussian filtering and fused with the two low-frequency images to get the low-frequency fusion image. After that, this fusion image is reversely transformed with the support sequence image fused by selecting the maximum gray. The final image is got at last. The result shows that compared with the simple support value transform fusion and the multi-scale top-hat decomposition fusion, the method suggested in this paper successfully increases the contrast ratio by 11.69%, decreases the distortion factor by 63.42%, and increases the local coarseness by 38.12%. All these show that the validity of fusion method proposed has been proved, which indicates that both bright and dim information from low-frequency images can effectively solve the contradiction between improving fused image's contrast ratio and reducing its' distortion after the both are fused and enhanced respectively, and then fused with the two low-frequency images, which provides a new useful method for improving the quality of fused inferred images.
本文提出了一种双色中波红外图像融合方法,以解决使用多尺度顶帽分解融合上述两种图像时对比度提升受限和边缘区域严重失真等频繁出现的问题。具体步骤如下:通过支撑值变换分别获得中波红外的两个细分波段图像的低频分量图像和一系列支撑值图像。首先使用多尺度顶帽分解方法分别从低频图像的最后一层提取多尺度亮暗信息。然后分别通过选择中波红外两个细分波段图像中每个像素的最大灰度值进行融合。接着,对得到的两幅图像进行灰度归一化和高斯滤波增强,并与两个低频图像融合得到低频融合图像。之后,对该融合图像与通过选择最大灰度值融合的支撑序列图像进行逆变换。最终得到最终图像。结果表明,与简单支撑值变换融合和多尺度顶帽分解融合相比,本文提出的方法成功将对比度提高了11.69%,将失真因子降低了63.42%,并将局部粗糙度提高了38.12%。所有这些都表明所提出的融合方法的有效性得到了证明,这表明低频图像的亮暗信息在分别融合和增强后,能够有效解决融合图像对比度提升与失真降低之间的矛盾,然后与两个低频图像融合,为提高融合推断图像的质量提供了一种新的有效方法。