Inverse Problems Are Solvable on Real Number Signal Processing Hardware

Inverse problems are used to model numerous tasks in imaging sciences, in particular, they encompass any task to reconstruct data from measurements. Thus, the algorithmic solvability of inverse problems is of significant importance. The study of this question is inherently related to the underlying computing model and hardware, since the admissible operations of any implemented algorithm are defined by the computing model and the hardware. Turing machines provide the fundamental model of today's digital computers. However, it has been shown that Turing machines are incapable of solving finite dimensional inverse problems for any given accuracy. This stimulates the question of how powerful the computing model must be to enable the general solution of finite dimensional inverse problems. This paper investigates the general computation framework of Blum-Shub-Smale (BSS) machines which allows the processing and storage of arbitrary real values. Although a corresponding real world computing device does not exist at the moment, research and development towards real number computing hardware, usually referred to by the term "neuromorphic computing", has increased in recent years. In this work, we show that real number computing in the framework of BSS machines does enable the algorithmic solvability of finite dimensional inverse problems. Our results emphasize the influence of the considered computing model in questions of algorithmic solvability of inverse problems.