Abstract
In order to obtain a panoramic image which is clearer, and has more layers and texture features, we propose an innovative multi-focus image fusion algorithm by combining with non-subsampled shearlet transform (NSST) and residual network (ResNet). First, NSST decomposes a pair of input images to produce subband coefficients of different frequencies for subsequent feature processing. Then, ResNet is applied to fuse the low frequency subband coefficients, and improved gradient sum of Laplace energy (IGSML) perform high frequency feature information processing. Finally, the inverse NSST is performed on the fused coefficients of different frequencies to obtain the final fused image. In our method, we fully consider the low frequency global features and high frequency detail information in image by using NSST. For low-frequency coefficients fusion, we can also obtain the spatial information features of low-frequency coefficient images by using ResNet, which has a deep network structure. IGSML can use different directional gradients to process high-frequency subband coefficients of different levels and directions, which is more conducive to the fusion of the coefficients. The experiment results show that the proposed method has been improved in the structural features and edge texture in the fusion images.
Highlights
In the field of digital image processing, different imaging devices acquire different information from the same scene
In order to improve the disadvantages of the above algorithms, we propose an innovative algorithm by combining with non-subsampled shearlet transform (NSST) and residual network (ResNet)
Based on the theory of deep learning, we propose a multifocus image algorithm by combining with NSST and ResNet
Summary
In the field of digital image processing, different imaging devices acquire different information from the same scene. Compared with the traditional spatial multi-focus image fusion methods, the deep learning algorithm has a great development prospect in optimizing the fusion image result processing [22]. In [24], a multi-focus image fusion algorithm, which uses image segmentation based on multiscale CNN to generate fusion decision map, generate high quality fused image. We use ResNet to deal with the shortcomings of insufficient image feature extraction based on CNN image fusion algorithm He et al [27] proposed ResNet, and the structure of ResNet has high training efficiency and the model accuracy has been greatly improved. In order to make the process result has more global contents and detail edge structure of the source images, NSST is used to divide the frequency of the original images, and separately fuse different coefficients. The sixth part summaizes the innovations and shortcomings of our method
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