Sinogram Enhancement for Tomographic Sensing Systems with Limited Resources

In this work we propose a novel method to address imaging with incomplete data from Tomography imaging sensors by utilizing techniques from the area of image processing. To achieve this, the problem of incomplete projection data is formulated as a sinogram recovery problem. The method is introduced and demonstrated in the case of noisy signals in limited-angle and sparse-angle tomography. The full measurement space (the sinogram) is treated as an image, pixellated according to the attainable spatial resolution, which allows the application of algorithms developed for image processing. Hough transform (HT) is used to estimate the missing projections by identifying the major sinusoidal patterns corresponding to higher intensity clusters in the object space. This method bypasses the traditional cumbersome interpolation techniques and the laborious computation of long iterations in solving the inverse problem.