Image representation by blob and its application in CT reconstruction from few projections
This work addresses the problem of CT reconstruction from limited data, offering a potential improvement over pixel-based methods for medical imaging applications.
The paper develops image representation models using blob basis functions for CT reconstruction from few projections, achieving improved reconstruction quality through TV or L1 norm minimization of blob coefficients.
The localized radial symmetric function, or blob, is an ideal alternative to the pixel basis for X-ray computed tomography (CT) image reconstruction. In this paper we develop image representation models using blob, and propose reconstruction methods for few projections data. The image is represented in a shift invariant space generated by a Gaussian blob or a multiscale blob system of different frequency selectivity, and the reconstruction is done through minimizing the Total Variation or the 1 norm of blob coefficients. Some 2D numerical results are presented, where we use GPU platform for accelerating the X-ray projection and back-projection, the interpolation and the gradient computations.