Genetic programming (GP) is an evolutionary-based search paradigm that is well suited to automatically solve difficult design problems. The general principles of GP have been used to evolve mathematical functions, models, image operators, programs, and even antennas and lenses. Since GP evolves the syntax and structure of a solution, the evolutionary process can be carried out in one environment and the solution can then be ported to another. However, given the nature of GP it is common that the evolved designs are unorthodox compared to traditional approaches used in the problem domain. Therefore, efficiently porting, improving or optimizing an evolved design might not be a trivial task. In this work we argue that the same GP principles used to evolve the solution can then be used to optimize a particular new implementation of the design, following the Genetic Improvement approach. In particular, this paper presents a case study where evolved image operators are ported from Matlab to OpenCV, and then the source code is optimized an improved using Genetic Improvement of Software for Multiple Objectives (GISMOE). In the example we show that functional behavior is maintained (output image) while improving non-functional properties (computation time). Despite the fact that this first example is a simple case, it clearly illustrates the possibilities of using GP principles in two distinct stages of the software development process, from design to improved implementation.