This paper introduces a modified adaptive PCA learning method for image denoising, aiming to produce high-quality denoised image patches from a single image. The approach involves using sparse representation to obtain highly correlated image patches, which are then processed through matrix completion to achieve high-quality results. The framework learns appropriate basis functions to describe image patches after applying transform domain methods on noisy patches, focusing on geometric structures. The algorithm maps low-resolution (LR) patches to high-resolution (HR) patches, enabling representation of more patch patterns with a smaller training database. The method integrates local pixel grouping (LPG) and steering kernel regression (SKR) to enhance denoising performance, adjusting iterations based on noise levels. The paper also discusses related work, including wavelet transforms and sparse coding, and highlights the advantages of the proposed method in preserving image structures while reducing noise. Experimental results demonstrate the effectiveness of the LPG-PCA method in denoising, showing improved PSNR and MSE values compared to other techniques. The method's two-stage process, involving adaptive PCA and noise level adjustment, further enhances denoising performance, making it a viable solution for image restoration tasks.This paper introduces a modified adaptive PCA learning method for image denoising, aiming to produce high-quality denoised image patches from a single image. The approach involves using sparse representation to obtain highly correlated image patches, which are then processed through matrix completion to achieve high-quality results. The framework learns appropriate basis functions to describe image patches after applying transform domain methods on noisy patches, focusing on geometric structures. The algorithm maps low-resolution (LR) patches to high-resolution (HR) patches, enabling representation of more patch patterns with a smaller training database. The method integrates local pixel grouping (LPG) and steering kernel regression (SKR) to enhance denoising performance, adjusting iterations based on noise levels. The paper also discusses related work, including wavelet transforms and sparse coding, and highlights the advantages of the proposed method in preserving image structures while reducing noise. Experimental results demonstrate the effectiveness of the LPG-PCA method in denoising, showing improved PSNR and MSE values compared to other techniques. The method's two-stage process, involving adaptive PCA and noise level adjustment, further enhances denoising performance, making it a viable solution for image restoration tasks.