Pixel-wise infrared tone remapping for rapid adaptation to high dynamic range variations

Biological vision systems can perform target selection, pattern recognition, and dynamic range adaptation at capability levels far beyond that of human-designed methods. This paper applies a two-stage Biologically-Inspired Vision (BIV) model for image pre-processing and infrared tone remapping, derived from the visual pipeline of the hover y. The first stage performs spatially invariant, pixel-wise, intensity normalization, to intelligently compress scene dynamic range and enhance local contrasts using an adaptive gain control mechanism. The second stage of the model applies adaptive spatio-temporal filtering to reduce redundancy within image sequences. Our experiments demonstrate the strengths of the model on four practical tasks. For large targets, the model acts as a sophisticated edge extractor. The examples show the ability to retrieve the complete structure of a boat from sea clutter, increasing the global contrast factor by 165%. Secondly and thirdly, for small and weak-signature targets, segmentation is demonstrated. A filter is applied to track a 2x2 pixel dragon y without interruption, and a small maritime vessel, extracted as it passes in front of a larger vessel of similar emissivity. Finally, the power of the BIV model to rapidly compress dynamic range and normalize sudden changes in scene luminance is validated by means of incandescent pyrotechnic pellets launched from an aerial platform.