## 1 Introduction  
Noise removal of images is an important preprocessing step in systems that detect objects based on images or use algorithms, such as recognition and tracking. It is often difficult to remove noise in areas with many high-frequency components, such as edges and text in images [1, 2]. Various filtering techniques for noise removal to improve image quality and achieve excellent images have been proposed. Noise removal methods, such as Split Bregman-anisotropic total variation denoising (SBTV) and Split Bregman-isotropic total variation denoising (SBTV) [3], have been proven to effectively restore noisy images.  
Local steering kernel (LSK) [4, 5] is an excellent technique for resolving image noise and uncertainty by estimating the local area structure while preserving the features of the original image. In addition, the nonlocal means (NLMeans) [6] algorithm estimates the original image based on the similarity of two image patches, showing excellent performance and attracting significant attention. However, it is difficult to filter noise in the high-frequency region of the image using typical noise removal techniques as image data and noise are heavily mixed in the said region [7, 8, 9].  
The Wiener filter became one of the most prominent algorithms in the noise removal field after its proposal by [PERSON]. The Wiener filter is fundamentally based on calculating statistical estimates from input signals to generate desired signals. The simple structure and excellent efficiency of Wiener filters enable their use across many studies to improve performance.  
The 2D adaptive cuckoo search-based Wiener filter (2 DACSWF) [10] was proposed to reduce the noise of the contaminated images in the AWGN algorithm. This algorithm optimizes the weights of the Wiener filter using the adaptive cuckoo search (ACS) method for estimating the original image.  
The split Bregman isotropic total variation denoising (SBITV) [3] algorithm was proposed to improve total variation (TV) denoising. TV denoising is an effective method for restoring original images in images with substantial noise levels. However, the resulting images are prone to the staircase effect and edge component loss. SBITV improved such functions using the split Bregman algorithm.  
The fuzzy membership function based modified Gaussian filter (FMGF) [2] was proposed to improve the performance of Gaussian filters when used, as AWGN removal algorithms. FMGF calculates the resulting images by adjusting the weighting of the Gaussian filter according to the fuzzy membership functions. Blurring occurs less in edges and text areas that include the primary data of the image.  
This paper proposed a modified steering kernel filter algorithm that utilizes image matching to minimizing smoothing, which occurs during the filtering process of the localization techniques. The proposed algorithm sets the weights by comparing the similarities of the localized regions to improve the local steering kernel method, which uses the directional changes in the pixel values of localized regions. Additionally, the proposed algorithm was applied to image matching procedures for comparing similarities. It determines weights based on the distribution characteristics of the pixel values of the center windows inside the matching areas and matching windows. The size of the center and matching windows were adjusted depending on the noise level of the image using adaptive window sizes. As the mask size was increased, the reduction of noise was increased whereas a smaller mask size pertained the original image details. The proposed algorithm was compared with conventional methods through analysis of simulations and usage of PSNR and SSIM, with the results showing the superior noise reduction of the proposed method compared to the other methods.