In the past decade, sufficient powerful Denoising algorithms have been devised - among them the patch-based nonlocal schemes, such as BM3D, have shown outstanding performance The BM3D employs a non-local modeling of images by collecting similar image patches in 3D arrays. The so-called collaborative filtering applied on such a 3D array is realized by transform-domain shrinkage. The block-matching with 3D transform domain collaborative filtering (BM3D) achieves very good performance in image Denoising. However, BM3D becomes ineffective when an image is heavily contaminated by noise. This is because it allows block-matching to search out of the region where a template block is located, resulting in poor matching. To address this, paper proposes an approach that is an extension of BM3D to represent to volumetric data & image reconstruction.

Modified BM3D, Volumetric Data, Image reconstruction

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