Image processing is found in many fields and in many domains. Advances in
digital image capturing technology allows for faster video rates, of higher quality, than has been seen before and that trend continues. With greater resolution and increased data flow there is also a need for faster and better hardware for image processing. As the trend introduced in Moore's law is slowing down, and possibly reaching saturation in the coming years, there is an ongoing search for new and different solutions in processor architecture. The trend went from single core to multi core and many core and now we are looking into other designs like memory streaming architectures and runtime reconfigurable computers. This thesis designs, implements and evaluates a programming interface for a dynamically-reconfigurable memory-streaming platform for image processing with a focus on programmability, power consumption, reconfigurability and performance. An application programming interface (API) is created to aid with new code development for the platform. The API is a library of functions that are run on an ARM processor and are used to setup, and communicate with, a stream of ρ-VEX soft processors running on a field programmable gate array (FPGA). In this research we look at other state-of-the-art solutions, for comparison and inspiration, that focus on programmability, reconfiguration and performance. The platform is reconfigurable at runtime and experiments show that it takes under 200 ms to completely reconfigure the fabric and initialize a new configuration of ρ-VEX processors. The platform is tested on a Zynq-7000 chip from Xilinx. Comparison is made between streaming architecture and a many core setup using the same amount of ρ-VEX soft processors. The results show a speedup of factor of 2 by using a single processing stream of seven cores compared with seven cores individually running the same algorithm. The result is a working fully-programmable and open-source streaming platform for the image processing domain.