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The existing state-of-the-art in image denoising is reflected by patch-based approaches such as Block Matching and 3D collaborative filtering (BM3D) algorithms. BM3D, however, still suffers from performance degradation in smooth areas as well as loss of image information, especially in the presence of high noise levels. Integrating shape adaptive techniques with BM3D increases the denoising effect, including the denoted image's visual quality; and retains image information as well. We proposed a system in this study that generates multiple images using different shapes. These images were aggregated for both stages in BM3D at the pixel or patch levels and, when properly aggregated, resulted in an average of 1.15 dB better denoising output than BM3D.