Deconvolution Using Projections Onto The Epigraph Set of a Convex Cost Function

Abstract

A new deconvolution algorithm based on orthogonal projections onto the epigraph set of a convex cost function is presented. In this algorithm, the dimension of the minimization problem is lifted by one and sets corresponding to the cost function are defined. As the utilized cost function is a convex function in $R^N$, the corresponding epigraph set is also a convex set in $R^{N+1}$. The deconvolution algorithm starts with an arbitrary initial estimate in $R^{N+1}$. At each step of the iterative algorithm, first deconvolution projections are performed onto the epigraphs, later an orthogonal projection is performed onto one of the constraint sets associated with the cost function in a sequential manner. The method provides globally optimal solutions for total-variation, $\ell_1$, $\ell_2$, and entropic cost functions.