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We deal with the reconstruction of the high-resolution (HR) computed tomography (CT) image from the CT projection data (Sinogram). Spatial resolution is one of the important parameters of CT images. Spatial resolution is a measure of how close to each other two objects that can still be distinguished. The spatial resolution of CT images depends on the field of view which in turn depends on the number of projections and the number of samples per projection. One way to increase the spatial resolution of the reconstructed images is to reduce the pixel size; however, this may require hardware alteration. Another way to increase the spatial resolution is to apply super-resolution (SR) technique on the lowresolution (LR) images. The conventional method for resolution enhancement is to apply SR technique as a post-process after LR CT images reconstruction. One drawback of this conventional method is that; performing two consequence steps, reconstruction and SR, requires more parameters to be tuned. Unlike the conventional method, we propose to simultaneously estimate HR image with the reconstruction step from the CT projection data, which indeed reduce the overall processing time and the required tuned parameters. On the other hand, even if the back-projection (BP) method is attractive because of its simplicity and low computational cost, it produces sub-optimal images with respect to artifacts, resolution, and noise. On contrast, iterative image reconstruction allows to easily model constraints and to incorporate prior knowledge which leads to better image quality. Therefore, in this paper, we employ the iterative reconstruction based on regularized Kaczmarz minimization algorithm, for its fast convergence, in the resolution enhancement as a post-process as well as simultaneous reconstruction and resolution enhancement methods.

Reconstruction of High Resolution Computed Tomography Image from Sinogram Space Using Adaptive Row Projection