This paper proposes a modified adaptive PCA learning method for image denoising, which aims to obtain high-quality denoised image patches using a single image. The method involves learning highly correlated image patches through sparse representation, followed by matrix completion to generate high-quality image patches. The framework uses a learning-based approach to find an appropriate basis function to describe image patches after applying transform domain methods on noisy image patches. The algorithm maps have been applied on LR patch space to generate the HR one, generating HR patch. This strategy allows more patch patterns to be represented using a smaller training database. In super-resolution (SR), the goal is not sparse representation, but sparse recovery. The paper introduces modifications to the local window before performing PCA transform, including adjusting the number of iterations based on the noise level and using steering kernel regression (SKR) to prepare the noisy image before applying LPG-PCA. Kernel regression (KR) is a nonparametric approach that requires minimal assumptions, making it suitable for regression problems. The method involves learning an orthogonal basis from the noisy image using PCA and decomposing the noisy patch in this basis. The denoised patch is obtained by zeroing all the small coefficients in the representation of the noisy patch in the learned basis. The LPG-PCA denoising procedure is iterated to further improve denoising performance, with the noise level adaptively adjusted in the second stage. The paper compares the performance of the proposed LPG-PCA method with other denoising techniques, such as ISKR and KLLD, on test images. Experimental results show that the LPG-PCA method achieves competitive denoising performance, especially in preserving image fine structures. The method outperforms other techniques in terms of PSNR and MSE, demonstrating its effectiveness in noise removal and image restoration. The results indicate that the LPG-PCA method is a promising approach for image denoising, particularly in preserving local image structures and reducing noise.This paper proposes a modified adaptive PCA learning method for image denoising, which aims to obtain high-quality denoised image patches using a single image. The method involves learning highly correlated image patches through sparse representation, followed by matrix completion to generate high-quality image patches. The framework uses a learning-based approach to find an appropriate basis function to describe image patches after applying transform domain methods on noisy image patches. The algorithm maps have been applied on LR patch space to generate the HR one, generating HR patch. This strategy allows more patch patterns to be represented using a smaller training database. In super-resolution (SR), the goal is not sparse representation, but sparse recovery. The paper introduces modifications to the local window before performing PCA transform, including adjusting the number of iterations based on the noise level and using steering kernel regression (SKR) to prepare the noisy image before applying LPG-PCA. Kernel regression (KR) is a nonparametric approach that requires minimal assumptions, making it suitable for regression problems. The method involves learning an orthogonal basis from the noisy image using PCA and decomposing the noisy patch in this basis. The denoised patch is obtained by zeroing all the small coefficients in the representation of the noisy patch in the learned basis. The LPG-PCA denoising procedure is iterated to further improve denoising performance, with the noise level adaptively adjusted in the second stage. The paper compares the performance of the proposed LPG-PCA method with other denoising techniques, such as ISKR and KLLD, on test images. Experimental results show that the LPG-PCA method achieves competitive denoising performance, especially in preserving image fine structures. The method outperforms other techniques in terms of PSNR and MSE, demonstrating its effectiveness in noise removal and image restoration. The results indicate that the LPG-PCA method is a promising approach for image denoising, particularly in preserving local image structures and reducing noise.