Limited-angle tomography and few-view tomography are extremely ill-conditioned problems. A usual way to reconstruct an image in limited-angle tomography or in few-view tomography is via an iterative algorithm that minimizes a two-term objective function. The objective function consists of a data fidelity term and a Bayesian term. A common Bayesian term is the total variation (TV) norm of the image. This paper proposes a different way to reconstruct the image in limited-angle tomography and few-view tomography by iteratively deconvolving the point spread functions (PSFs). Two PSFs are considered: the PSF of the filtered backprojection (FBP) and the PSF of pure backprojection. The method of using the PSF of pure backprojection gives better results for both limited-angle tomography and few-view tomography studies.