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.
Super resolution reconstruction (SRR) improves resolution by increasing the effective sampling frequency. Target acquisition range increases but the amount of increase depends upon the relationship between the optical blur diameter and the detector size. Range improvement of up to 52% is possible.
Modern systems digitize the scene into 12 or more bits but the display typically presents only 8 bits. Gray scale compression forces scene detail to fall into a gray level and thereby "disappear." Local area processing (LAP) readjusts the gray scale so that scene detail becomes discernible. Without LAP the target signature is small compared to the global scene dynamic range and this results in poor range performance. With LAP, the target contrast is large compared to the local background. The combination of SRR and LAP significantly increases range performance.
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.