## 1 Introduction
This sensitivity can lead to either insufficient noise suppression or excessive blurring. Additionally, it may struggle with non-homogeneous textures, resulting in uneven noise reduction across varying image regions. Recently, a Log Compression filter [15] has been used for speckle-noise suppression by applying a logarithmic transformation to the image, which compresses the dynamic range and reduces the impact of speckle noise. This technique enhances image contrast and helps in noise reduction by transforming multiplicative noise into additive noise, which is easier to manage. However, its limitations include a potential loss of image details and contrast in high-intensity areas due to the compression effect. Additionally, log compression may not be effective in preserving fine structural details, particularly in images with a wide range of intensities, leading to potential artifacts or reduced detail fidelity. Despite the fact that a few of the above-mentioned filters [10; 12; 13; 14] are traditional, these continue to be widely used in research as benchmark methods for evaluating and comparing the performance of newer despeckling techniques [16; 17; 18; 19]. Recently, the most common methods for speckle noise mitigation in SAR images can be broadly classified into two categories: thresholding-based and diffusion-based techniques. Thresholding methods, including traditional and fusion-based approaches, focus on separating noise from useful signals by setting thresholds to identify and suppress noise [20]. Fusion-based thresholding improves upon this by combining multiple thresholding strategies, leading to more effective noise isolation [21]. In [22], a technique based on wavelets was developed to address the multiplicative characteristics of noise present in radar images. The low-pass and high-pass filters for further despeckling need to decompose the transformed image using the discrete wavelet transform (DWT) method. Also, it helps to classify the images into two categories: the approximate part and the detailed part of the images. The absolute mean deviation and threshold value assist in determining the efficiency of the model at different noise variances. This model works with the limitation because the resultant images still carry the noise, which leads to blurred edges; the authors in [23] propose a novel SAR image classification framework that tackles the challenge of speckle noise by using multi-feature fusion and adaptive kernel combination. They enhance classification accuracy by integrating texture, contextual, and spatial information through a newly defined non-negative logarithmic likelihood difference metric and local consistency optimization. However, the approach has limitations in handling extremely noisy environments or varying noise levels across different images, as the adaptive methods may not fully account for all variability in real-world scenarios. The despeckling filter proposed in [24] uses a Bayesian filter based on direction lets, augmented by a homomorphic filtering approach specifically designed for SAR images. The homomorphic directionless elements are modeled to exhibit a Cauchy distribution, while granular pattern noise is characterized by a Gaussian distribution. These two methods are integrated with the addition of maximum a posteriori computation operation to estimate the noiseless components. The efficiency rate is accepted in terms of dealing with the granular form of noise. In [25], the concept of despeckling related to the granular form using curvelet transform was brought in, as well as an improved practical swarm reduction mechanism for enhancing the image characteristics was brought in. The practical swarm reduction mechanism has been integrated because it globally leads to granular pattern removal and image correctness. It is observed as quite a good model in terms of taking care of the granular pattern of the noise.