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Computed Tomography Image Denoising by Learning to Separate Morphological Diversity Publisher



Khodabandeh A1 ; Alirezaie J1 ; Babyn P2 ; Ahmadian A3
Authors

Source: 2015 38th International Conference on Telecommunications and Signal Processing# TSP 2015 Published:2015


Abstract

Computed Tomography (CT) image denoising is a challenging topic because of the difficulty in modeling noise. In this paper, we propose an image decomposition approach to remove noise from low-dose CT images. We model the image as y = X1 + X2 where the main structures and noise are two superimposed layers. Total Variation (TV) minimization is used to learn two dictionaries to represent structure and noise respectively and sparse coding is used to separate x1 and x2. Finally, an iterative post-processing stage is introduced that uses image-adapted curvelet dictionaries to recover edges. Our results demonstrate that image separation is a viable alternative to the classic K-SVD denoising method. © 2015 IEEE.
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