In this work, multiscale decomposition and sparse representation-based multimodal medical image fusion technique is proposed. An efficient denoising technique, feature-preserving regularized Savitzky–Golay filter is applied to obtain noise-free images. The filtered medical images are split into low- and high-pass subbands by non-subsampled shearlet transform (NSST). The sparse coefficient vectors of low-pass subbands are obtained from a pre-learned dictionary, and “max-L1” rule is applied to obtain the fused low-pass subband. However, high-pass subbands are fused using “max-absolute” rule. Lastly, NSST reconstruction is applied to generate the fused multimodal medical image. The non-subsampled contourlet transform, NSST-based fusion using parameter adaptive pulse coupled neural network and phase congruency techniques are also realized for comparative analysis. Multiple experiments on clean and noisy sets are performed for gray and color medical images. The fusion techniques are also tested on infrared–visible image pairs. The visual and quantitative outcomes verify that suggested technique outperforms the state-of-the-art fusion techniques.

中文翻译：

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使用稀疏表示的基于FPRSGF去噪非下采样剪切波变换的图像融合

在这项工作中，提出了基于多尺度分解和稀疏表示的多模态医学图像融合技术。一种有效的去噪技术，特征保留正则化 Savitzky-Golay 滤波器被应用于获得无噪声图像。过滤后的医学图像通过非下采样剪切波变换 (NSST) 分为低通和高通子带。低通子带的稀疏系数向量是从预先学习的字典中获得的，并应用“max-L1”规则来获得融合的低通子带。然而，高通子带使用“最大绝对”规则融合。最后，应用 NSST 重建来生成融合的多模态医学图像。非下采样轮廓波变换，还实现了使用参数自适应脉冲耦合神经网络和相位一致性技术的基于 NSST 的融合，用于比较分析。对灰度和彩色医学图像在干净和嘈杂的集合上进行了多次实验。融合技术也在红外-可见光图像对上进行了测试。视觉和定量结果证明建议的技术优于最先进的融合技术。