Computed Tomography Images Restoration Using Anisotropic Diffusion Regularization
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The CT scan imaging system is one of the most interesting non-invasive radiological methods allowing the generation of tomographic images of all parts of the human body. However, CT images are corrupted by noise and blur due to the imperfections and the physical limitations of the imaging systems. Increasing the spatial resolution of these images leads to a good interpretation by the clinician. In this paper, we propose a new approach to improve the quality of the CT images. Our method is based on the anisotropic diffusion regularisation incorporates an adaptative smoothness constraint in the deconvolution process. That is, the smooth is encouraged in a homogeneous region and discourage across boundaries, in order to preserve significant image details. The blur component is estimated by an iterative blind deconvolution approach and incorporated in the restoration process. Experimental results show a good performance and are very promising for future research.
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