The range of luminance levels in the natural world varies in the order of 108, significantly larger than the 8-bits
employed by most digital imaging systems. To overcome their limited dynamic range traditional systems rely on the fact
that the dynamic range of a scene is typically much lower, and by adjusting a global gain factor (shutter speed) it is
possible to acquire usable images. However in many situations 8-bits of dynamic range is insufficient, meaning
potentially useful information, lying outside of the dynamic range of the device, is lost. Traditional approaches to
solving this have involved using nonlinear gamma tables to compress the range, hence reducing contrast in the digitized
scene, or using 16-bit imaging devices, which use more bandwidth and are incompatible with most recording media and
software post-processing techniques. This paper describes an algorithm, based on biological vision, which overcomes
many of these problems. The algorithm reduces the redundancy of visual information and compresses the data observed
in the real world into a significantly lower bandwidth signal, better suited for traditional 8-bit image processing and
display. However, most importantly, no potentially useful information is lost and the contrast of the scene is enhanced in
areas of high informational content (where there are changes) and reduced in areas containing low information content
(where there are no changes). Thus making higher-order tasks, such as object identification and tracking, easier as
redundant information has already been removed.
This paper describes the implementation of a robust adaptive photodetector circuit that mimics the characteristics of insect photoreceptors. The implementation of the photodetector circuit is an elaborated version of the mathematical model initially developed by van Hateren and Snippe. It consists of a linear photodetector, two divisive feedback loops and a static non-linearity stage. The photoreceptor circuit was rigorously tested under both steady-state and dynamic (natural scenes) conditions and the circuit parameters optimized such that the output was highly correlated to results obtained from fly photoreceptors observing an identical stimulus. The results show that this adaptive non-linear photoreceptor circuit is ideally suited to mimic the biological photoreceptors found in insects.
An adaptive non-linear photodetector circuit is implemented using electronic discrete components to describe the response of blowfly photoreceptor cells. The photodetector circuit consists of a cascade of a linear photodetector, two divisive feedback loops and a static non-linearity stage. The circuit is rigorously evaluated using an ultra bright Light Emitting Diode. Detailed comparison is done between the photodetector circuit and the actual neurobiological data of the blowfly photoreceptor cells to fine tune the parameters of the circuit.