Research on Image Fusion Algorithm Based on NSST Frequency Division and Improved LSCN

Single modal medical images provide limited information and cannot reflect all the details of the relevant tissues, which may lead to misdiagnosis in clinical medicine. Therefore, a medical image fusion algorithm based on non-down-sampling shear wave transform (NSST) is proposed. This algorithm fuses multi-modal medical images, enriches the information of fused images, improves the image quality, and provides a basis for clinical diagnosis. Firstly, the low-frequency sub-band and several high-frequency directional sub-bands are obtained by NSST transformation of the source image, and the structural similarity between sub-bands is evaluated. Then, according to the characteristics of low-frequency sub-band images, for sub-images with high similarity, the regional features are obtained by region energy and variance, and the fusion method is based on region feature weighting. For sub-images with low similarity, two images to be fused are input into the LSCN model respectively by fusing the connection items of the improved LSCN model. The improved L-term replaces the ignition frequency in the traditional PCNN as the output. According to the characteristics of high frequency sub-images, the fusion rule of combining visual sensitivity coefficient and regional energy is adopted for sub-images with high similarity. For sub-images with low similarity, an improved guided filter is used to fuse the sub-images in order to maintain the clear edges of the images. Finally, the image is reconstructed by inverse NSST transform. The experimental results show that the proposed algorithm can obtain better fusion effects in both objective and subjective evaluation. The obtained fusion image has rich information, excellent edge retention characteristics, subjectively clear texture and high contrast and good visual effect.