提出一种红外与微光图像融合新算法,即在Tetrolet变换下基于柯西模糊函数的融合算法,用于改善融合图像对比度低、细节信息缺失、融合效果较差等问题。首先,源图像通过Tetrolet变换,分解出两组高低频系数;然后,针对低频系数,提出区域能量-柯西模糊函数规则,针对高频系数,采用区域拉普拉斯能量和进行处理;最后,利用Tetrolet逆变换将得到的新高低频系数转换为最终的融合结果。实验结果表明,本文方法在提高融合图像对比度、保留图像细节等方面具有一定的优势。

In order to improve the problems of low contrast, lack of detailed information, and poor fusion effect of the fusion image, a new algorithm for infrared and low light image fusion, that is, a fusion algorithm based on Cauchy fuzzy function under Tetrolet transform, is proposed. First, the source image is decomposed into two sets of high-frequency and low-frequency coefficients by Tetrolet transform; then, a regional energy-Cauchy fuzzy function rule is proposed for the low-frequency coefficients and the regional Laplacian energy sum is used for high-frequency coefficients; finally, the inverse Tetrolet transform is used to transform the new high-frequency and low frequency coefficients into the final fusion results. Experimental results show that the proposed method has certain advantages in improving the contrast of fused images and preserving image details.