## 1 Introduction  
In this digital era, image processing plays a vital role, and it is essential to study the various approaches to it. Initially, texture analysis concentrates on statistical measures of texture measured from the Grey Level Co-occurrence Matrix (_GLCM_) [1; 2]. Texture is one of the visually significant characteristics to enrich the source information for further analysis. Image recognition has many applications, including remote sensing, content-based image representation, automatic inspection of data, and so on. Texture is a feature extraction that identifies structural, geometrical, statistical, and model-based representations [3]. The traditional image fusion approaches include PA-DCPCNN and MST-CSR. The performance of the PA-DCPCNN method is limited due to the inbuilt demerits of the Non-subsampled Contourlet Transform (NSCT) and Pulse Coupled Neural Network (PCNN). The effective fusion rule based on MST-CSR can be developed to achieve better fusion performance. To analyze and characterize the information, wavelets are most widely used. In this proposedmethod, precise and unified framework activity is constructed along with hybrid wavelets with the combination of _SWT_[4; 5] and _DTCWT_[6; 7; 8; 9]. Further, for efficient information extraction, texture features are applied [10; 11; 12]. This procedure for framework activity is described in Section 2. Several approaches to _MIF_ have been proposed over the years. For example, [PERSON] et al. proposed the usage of Spatial Pyramid Zernike Moments (_SPZM_) and Law's Textures Feature Set (_LTFS_)-based facial expression recognition system to describe the patterns of facial region changes for movement in the muzzles of the face. This work is constructed with multilayer perception and feedforward neural networks. An experiment is carried out to demonstrate concatenating feature vectors of facial expression with better accuracy and a low error rate [13]. [PERSON] et al. proposed nonsubsampled shearlet transform (_NSST_) and _TEM_ to extract helpful information that is practical and valuable related to the study of human body structure. In this work, the luminance, in-phase, quadrature (_YIQ_) colour model is implied for functional information confinement. The visual analysis is done with five quality metrics such as spatial frequency (_SF_), edge-intensity (_EI_), mutual information (_MI_), visual information fidelity (_VIF_), and structural similarity index measure (_SSIM_) [14]. In medical imaging, clinical procedures essential to the diagnosis of disease and surgery using image fusion. Medical procedures demand high accuracy. This work is implemented with the combination of _SWT_ and _TEM_ by [PERSON] et al. The superiority of the work is proved with the help of fusion quality metrics, and practical analysis tasks are studied [15]. In multi-resolution (_MR_) fusion, to describe the pattern of an image, Gaussian Markov random fields (_GMRF_) and Gibbs random fields (_GRF_) play vital roles in characterizing the texture. This work was implemented with [PERSON]'s mask descriptor for _MR_ images and a k-nearest neighborhood classifier for classifying each texture pattern into appropriate classes by [PERSON] et al. [16]. [PERSON] et al. concentrate on _TEM_ laws for feature extraction and artificial neural network (_ANN_) for classification. The superiority of this work is proved by comparing it with other procedures in terms of accuracy [17]. The approaches cited in the literature are compared to the proposed methodology both qualitatively and quantitatively [4; 18; 19; 20; 21; 22]. From the above literature study, it is identified that the following are weak points: lack of spatial distortion, image blurring, low efficiency of the method, results related to visual and quantitative quality needing improvement, lack of shift-invariance, and poor directional selectivity.