It is critical in military applications to be able to extract features in imagery that may be of interest to
the viewer at any time of the day or night. Infrared (IR) imagery is ideally suited for producing
these types of images. However, even under the best of circumstances, the traditional approach of
applying a global automatic gain control (AGC) to the digital image may not provide the user with
local area details that may be of interest. Processing the imagery locally can enhance additional
features and characteristics in the image which provide the viewer with an improved understanding
of the scene being observed. This paper describes a multi-resolution pyramid approach for
decomposing an image, enhancing its contrast by remapping the histograms to desired pdfs, filtering
them and recombining them to create an output image with much more visible detail than the input
image. The technique improves the local area image contrast in light and dark areas providing the
warfighter with significantly improved situational awareness.
Through the trade-off temporal information, a significant increase in spatial resolution is obtainable. This improvement
is quantifiable by using Airy's disc analysis against camera sensor pitch. Integrate the use of Airy's disc to quantify the
image improvement in resolvability and ultimately system range. It this comparison that sets the ground works for
realistic expectation. Our SR system is a natural tracker of moving vehicles with the addition of improved target
resolvability. Super Resolution can capitalize on camera platforms instability. A by product of SR is digitally stabilize
imagery to a fraction of a sub-pixel. Investigation in the sub-pixel remapping has lead to the developed of improved
super resolve images. Another, approach has lead to the development of a window management scheme for further
improvement. The cleaner, from a noise and structural point-of-view, the composite SR image is the more favorable it is
to high-sharpening. Mapping into a transform space greatly reduces the correlation complexity which makes it easier to
realize the complete algorithm into hardware. We have implemented this system into a real-time architecture. The
hardware configuration is composed of an FPGA and supporting processor.
It is critical in surveillance applications to be able to extract features in imagery that may be of interest to the viewer at
any time of the day or night. Infrared (IR) imagery is ideally suited for producing these types of images. However, even
this imagery is not always optimal. Processing the imagery with a local area image operator can enhance additional
features and characteristics in the image that provide the viewer with an improved understanding of the scene being
observed. This paper discusses the development of two algorithms for image enhancement for infrared imagery using
local area processing. The enhancement algorithm extends theory previously developed for medical applications.
Algorithm differences addressed include application to IR imagery and to a panning camera rather than still imagery. It
also discusses the obstacles encountered and overcome for insertion of this algorithm into a 10" gimbaled midwave
infrared imaging system for a variety of real-time processing applications. This technology is directly applicable to
driver's vision enhancement systems as well as other night visions systems such as night vision goggles.