In the context of software engineering for image processing (IP), we consider the notion of reusability of algorithms. In many software tools, an algorithm's implementation often depends on the type of processed data. In a broad definition, discrete digital images may have various forms : classical 2D images, 3D volumes, non-regular graphs, cell complexes, and so on : thus leading to a combinatorial explosion of the theoretical number of implementations. Generic programming (GP) is a framework suited to the development of reusable software tools. We present a programming paradigm based on GP designed for the creation of scientific software such as IP tools. This approach combines the benefits of reusability, expressive power, extensibility, and efficiency. We then propose a software architecture for IP using this programming paradigm based on a generic IP library. The foundations of this framework define essential IP concepts, enabling the development of algorithms compatible with many image types. We finally present a strategy to build high-level tools on top of this library, such as bridges to dynamic languages or graphical user interfaces. This mechanism has been designed to preserve the genericity and efficiency of the underlying software tools, while making them simpler to use and more flexible
