Common Principles of Image Acquisition Systems and Biological Vision

In this paper, we argue that biological vision and electronic image acquisition share common principles despite their vastly different implementations. These shared principles are based on the need to acquire a common set of input stimuli as well as the need to generalize from the acquired images. Two related principles are discussed in detail, namely, multiple parallel image representations and the use of dedicated local memory in various stages of acquisition and processing. We review relevant literature in visual neuroscience and image systems engineering to support our argument. Particularly, the paper discusses multiple capture image acquisition, with applications such as dynamic range, field-of-view, or depth-of-field extension. Finally, as an example, a novel multiplecapture-single-image complementary metal–oxide–semiconductor sensor is presented. This sensor has been developed at Stanford University and it illustrates the principles that are shared among biological vision and image acquisition.