针对传统NSCT(非下采样轮廓波变换)算法中NSP(多尺度分解方法)对细节信息捕捉能力较差及利用其进行图像融合得到的融合图像出现细节丢失问题,提出改进的NSCT算法。不同于传统NSCT算法,该算法首先采用细节捕捉能力较强的非下采样形态学小波分解替代NSP分解,实现对源图像的多尺度分解,将源图像分解成水平高频、垂直高频、对角高频和低频4部分;然后利用NDFB(非下采样的方向性滤波器)对高频部分进行多方向分解得到一系列高频信息,实现改进型NSCT分解。实验结果表明,该算法的细节捕捉能力较传统算法好,在相同融合规则下其图像融合效果更好,各项融合指标值均有所提高,其中平均梯度提高了10%,且易于实现,可广泛用于多分辨率图像融合,是一种有效的融合图像算法。

The NSP (multi-scale decomposition method) of the traditional NSCT (non-subsampled contourlet transform) algorithm has a poor detail information capturing ability and when applied to image fusion it causes a loss of image details. In this paper, we present an improved NSCT algorithm. Different from the traditional NSCT algorithm, we adopt the non-subsampled morphological wavelet decomposition, which has a better detail capture capability, to realize a multi-scale decomposition of the source image and replacing the NSP decomposition. The source images are decomposed into four parts: low-frequency, horizontal high-frequency, vertical high-frequency, and diagonal high-frequency. Afterwards, the improved NSCT decomposition on high frequencies using the NDFB (non-subsampled directional filter) for multiple directions of decomposition is realized. Our experiments show that, compared with traditional algorithms, this algorithm has a better detail capturing ability, its image fusion effect is better under the same fusion rules, and all fusion indexes are improved. Among them, the average gradient is increased by 10%. This effective image fusion algorithm can be easily realized and widely used in multi-resolution image fusion.