In this paper, we present an image denoising algorithm comprising three stages. In the first stage, Principal Component Analysis (PCA) is used to suppress the noise. PCA is applied to image blocks to characterize localized features and rare image patches. In the second stage, we use the Gaussian curvature to develop an adaptive total-variation-based (TV) denoising model to effectively remove visual artifacts and noise residual generated by the first stage. Finally, the denoised image is sharpened in order to enhance the contrast of the denoising result. Experimental results on natural images and computed tomography (CT) images demonstrated that the proposed algorithm yields denoising results better than competing algorithms in terms of both qualitative and quantitative aspects.