Patch-based approaches such as Block Matching and 3D collaborative Filtering (BM3D) algorithm represent the current state-of-the-art in image denoising. However, BM3D still suffers from degradation in performance in smooth areas as well as loss of image details, specifically in the presence of high noise levels.
Integrating shape adaptive methods with BM3D improves the denoising outcome including the visual quality of the denoised image; and also maintains image details. In this study, we proposed a framework that produces multiple images using various shapes. These images were aggregated at the pixel or patch levels for both stages in BM3D, and when appropriately aggregated, resulted in better denoising performance than BM3D by 1.15 dB, on average.