Super-resolution of infrared images: does it improve operator object detection performance?

Abstract

The ability to detect dangerous objects (such as improvised explosive devices) from a distance is important in security and military environments. Standoff imaging can produce images that have been degraded by atmospheric turbulence, movement, blurring and other factors. The number and size of pixels in the imaging sensor can also contribute to image degradation through under-sampling of the image. Establishing processes that enhance degraded or under-sampled infrared images so that objects of interest can be recognised with more certainty is important. In this paper, super-resolution image reconstruction and deconvolution methods are explored, with an emphasis on quantifying and understanding human operator detection performance.