## 1 Introduction
proposed a unique MIF approach based on a super-resolution hybrid method [24]. In the DWT domain, [PERSON] et al. [25] proposed a novel MIF approach that uses a convolutional neural network (CNN) algorithm to combine the benefits of both spatial and transforms domain approaches. Instead of using image blocks or source images, CNN is employed to amplify features and build various decision maps for different frequency subbands. The additional benefit of the CNN approach is to utilize the adaptive fusion rule in the fusion process. [PERSON] et al. [26] derived two important feature metrics the energy of Laplacian and the variance of Laplacian. The idea of the proposed work is to evaluate the correlation coefficient between the source blocks and the artificial blurred blocks in the discrete cosine transform (DCT) domain using the focus metrics. A new approach for MIF is proposed by [PERSON] et al. [27]. A super-resolution method is concerned with contrast enhancement, SWT with the combination of Discrete Meyer filter for decomposition. The further final image is attained by implementing a new fusion rule with a gradient-based approach. Wavelet transformations are introduced by [PERSON] et al. [28] to extract high and low-frequency coefficients. In addition to this deep convolution, neural networks are implemented to generate a high-quality fused image by direct mapping in between learning high-frequency and low-frequency of source images [29]. [PERSON] et al. [30] presented a new focus metric based on the surface area of regions using the encircled method. This measure's ability to discriminate blurred regions in the fusion method is demonstrated. [PERSON] et al. [31] a novel fusion method that applies CNN to assist sparse representation (SR) is proposed for the purpose of gaining a fused image with more precise and abundant information. The computational complexity of this fusion method is impressively reduced. [PERSON] [32] proposed a novel MIF approach based on multiscale transform (MST) and convolution sparse representation (CSR) to address the inherent defects of both the MST and SR-based fusion methods. The proposed approach is put up against the approaches discussed in the literature [21; 22; 23; 24; 25; 26; 27; 28; 30]. The following are the essential contributions of this work:  
1. A hybrid method (i.e., qshiftN DTCWT and LP) with MPCA is introduced for the fusion of multifocus images;
2. The method helps combine multiple source images to develop a fused image having better image quality with good directionality, a high degree of shift-invariance, achieving better visual quality, and retaining more information than the source images;
3. Using the MPCA method, the amount of redundant data is decreased, and the most significant components of the source images are extracted;
4. Extend the depth-of-field (DOF) of the advanced imaging system;
5. An analyzing procedure has been done both quantitatively and qualitatively;
6. Proposed approach performance has improved compared with the state-of-the-art techniques developed in recent years. The rest of the paper is organized as follows: Section 2 explains the proposed fusion methodology as well as the fusion methods implemented. Section 3 presents the results of the experimentation. Section 4 concludes with conclusions.