## 1 Introduction
In [26], the framework has been designed to deal with the multiplicative nature of the granular form, implementing non-local filtering and wavelet shrinkage using random homogeneous metrics. The obtained outcomes of the proposed method show greater value in terms of PSNR in texture preservation as well as despeckling. Also, it deals precisely in terms of smoothing the pixels in homogeneous regions. It is compared and tested with some other non-local filtering methods using Bayesian approaches in both ultrasound and radar images. This work presents the effectiveness of non-local filtering in terms of reducing the granular form in radar images. In [27], the fusion-based granular pattern removing model was developed by implementing a directional smoothing technique and method-noise thresholding with the addition of the db2-based two-dimensional discrete wavelet transform (2D-DWT). The experimental results of [27] showed that tests on simulated and real radar images and were compared and validated using the performance metrics. The complete despeckling framework runs around the directional smoothing method, wavelet thresholding with the addition of the Bayesian shrinkage rule, and method-noise thresholding. In the decomposition process, the images are classified into two categories: approximate and detailed. The approximate parts obtained after decomposition and the relative factors are taken care of using the directional smoothing method. In the case of the detailed parts, the relative factors are taken care of by the wavelet thresholding with the addition of the method-noise thresholding. In [28], the statistical features are analyzed and implemented by the DWT to deal with granular patterns in radar images. The primary focus of existing techniques has been on enhancing image quality, often at the expense of retaining original image features and reducing noise. This trade-off can lead to the loss of fine details and biased post-processing outcomes. Diffusion-based techniques, particularly non-linear diffusion, focus on smoothing the image while preserving important structural features like edges [29]. The study of [30] introduced the concept of anisotropic diffusion and proposed a new description of scale-space and a class of processes used to understand a diffusion method. This technique is widespread in image processing. The main purpose of the proposed technique is to precisely deal with the granular form without losing the major characteristics of the images. In [31], the concept of an anisotropic diffusion-based method is intelligently enhanced and implemented on radar images for dealing with speckle noise. The proposed method is quite efficient in mean preservation, speckle-noise reduction, and edge detection and preservation, and the determined efficiency shows precise results compared to other despeckling filters and conventional anisotropic diffusion methods. The study of [32] proposed a filtering model that deals with the granular using median refined anisotropic diffusion. The purpose of implementing the median filter in the proposed method is to preserve the major characteristics of the images. With the anisotropic diffusion, the median filter is implemented to achieve an efficient result, as the median filter precisely deals with salt-and-paper noise. In [33], a method was developed to reduce the noise in a granular form by incorporating an enhanced anisotropic diffusion model. This model focuses on preserving edges by taking into account the local directional changes in intensity and grayscale levels. It has a good potential to remove the granular pattern in radar images. The resultant images still carried the noises, which decreased the efficiency of the model due to the high amount of darkness, which shows its limitation. As a result, the developed model efficiently works in terms of preserving the fine details like edges.