Challenges in digital imaging for artificial human vision

Several international research teams are currently developing artificial human vision ("bionic eye") systems that have the potential to restore some visual faculties to blind persons. Given the significant advancements from these research teams, it is conceivable that the implantation of a safe and useful prosthesis will occur soon, perhaps in the next 2 - 4 years. It is thus timely to suggest and demonstrate methods to increase the information content of such artificial vision systems. Several ideas are suggested in this paper, such as brightness modulation, range indication, importance mapping and the delivery of supplementary information, which will do much towards providing visual information comparable to that obtained via a normally functioning human eye but at far lower information rates. This paper briefly describes the framework of artificial vision systems and outlines basic considerations of digital image processing as applied to artificial vision systems. We describe the poor quality of anticipated images produced by these artificial vision systems and the need for enhancing the images to allow increased scene understanding. Several techniques are identified which could enhance the information content of images. We then describe our own research in this area, which aims to determine the performance envelope of useful low quality images associated with artificial vision systems. Our subjective assessment studies using representative test patterns have investigated how much information and what types of information are needed to recognise or perceive a scene. This testing has been to identify the most informative image processing operations which lead to better understanding of picture content.